KR20160147516A - Method for detecting arrhythmia information and device supporting the same - Google Patents

Abstract

According to an embodiment of the present invention, a method for detecting arrhythmia comprises the processes of: obtaining an electrocardiogram (ECG) signal; measuring a signal-to-noise ratio of the obtained ECG signal; and determining whether other biological signals are used in accordance with the measured signal-to-noise ratio.

Description

METHOD FOR DETECTING ARRHYTHMIA INFORMATION AND DEVICE SUPPORTING THE SAME,

The present invention relates to a method for detecting an arrhythmia, for example, and a device supporting the same.

As the level of living improves and the interest in health grows, "living long and healthy" is one of the greatest concerns for those who are living in the recent society. In addition, changes in diet, lack of exercise, as well as stress, yesterday, suddenly healthy people suddenly suffered from health problems or death due to diseases such as adult diseases, heart related diseases, or brain diseases.

In particular, arrhythmia is an abnormally rapid or irregular heart rhythm. Severe arrhythmia can result in cardiac arrest and death from heart attack.

In the prior art, arrhythmia is mainly performed by electrocardiogram measurement. For example, the number of heart beats and the like are measured through a plurality of electrodes and lead wires, and an electrocardiogram measuring instrument combined therewith, and the presence or absence of arrhythmia is determined based on the measured heart rate.

However, in the electrocardiogram measurement according to the related art, when the electrode is not attached properly, the electrode is not adhered due to long-term attachment, or noise due to movement of the user occurs, have.

Various embodiments of the present invention are directed to a method for detecting an arrhythmia that improves the accuracy of arrhythmia detection using an echocardiogram signal or a heart sound signal in addition to an electrocardiogram signal and a device for supporting the same.

A method for detecting an arrhythmia according to an embodiment of the present invention includes the steps of acquiring an electrocardiogram signal; Calculating a signal-to-noise ratio of the obtained electrocardiogram signal; And determining whether to use another bio-signal according to the calculated signal-to-noise ratio.

In one embodiment, the other vital signal may comprise at least one of a cardiac tidal signal and a cardiac signal.

In one embodiment, the step of determining whether to use the other bio-signal in accordance with the calculated signal-to-noise ratio may comprise the steps of: comparing the calculated signal-to-noise ratio and a designated first threshold value; Detecting a feature point of the electrocardiogram signal using the another bio-signal when the signal-to-noise ratio is less than or equal to the designated first threshold value; and determining whether the arrhythmia is generated based on the detected feature point of the electrocardiogram signal .

In one embodiment, the step of detecting feature points of the electrocardiogram signal using the other bio-signals may include the steps of detecting feature points of the other bio-signals, and using the detected feature points of the other bio- Wherein the step of detecting an R peak point of the electrocardiogram signal includes the steps of specifying a certain time range from a detected characteristic point of the other bio signal, And determining a voltage peak point of the electrocardiogram signal within the predetermined time range from the detected characteristic point as the R peak point.

In one embodiment, the detected feature point of the other bio-signal may comprise at least one of a J peak point of the cardiac trajectory signal and a peak point corresponding to a ventricular systole of the cardiac signal.

In one embodiment, the step of determining whether to use the other bio-signals in accordance with the calculated signal-to-noise ratio comprises the steps of: comparing the calculated signal-to-noise ratio and a designated second threshold value; If the measured signal-to-noise ratio is equal to or less than the second threshold value, whether the arrhythmia is generated based on the other bio-signal instead of the electrocardiogram signal.

In one embodiment, the other bio-signal is a cardiac troposphere signal, based on at least one of the electrocardiogram signal and the cardiac troposphere signal, determining the occurrence of arrhythmia, based on the electrocardiogram signal and the cardiac trajectory signal A process of calculating the blood pressure, determining whether the arrhythmia has occurred and the risk of an arrhythmia based on the calculated blood pressure, and transmitting the notification information through the communication unit.

In one embodiment, the method further includes a step of determining an item out of a preset range of a user's biometric information or external environment information as an argument for causing an arrhythmia upon detection of an arrhythmia, and a step of storing a parameter for causing the determined arrhythmia .

In one embodiment, the item may include at least one of blood pressure, stress, temperature, caffeine intake, alcohol consumption, smoking amount, sleep state.

In one embodiment, there is provided a method for diagnosing an arrhythmia, comprising the steps of: determining an occurrence frequency or accumulation amount of an item that deviates from a predetermined range in a user's biometric information or external environment information during a certain period of time to a threshold value to determine an arrhythmia- And a step of storing an arrhythmia-inducing factor.

In one embodiment, the biometric information or the external environment information of the current user is measured, and if the measured biometric information or external environment information of the current user corresponds to a factor causing the arrhythmia, And outputting a notification to the user.

An arrhythmia detection apparatus according to an embodiment of the present invention includes a sensor unit for acquiring an electrocardiogram signal and a control unit, and the controller calculates a signal-to-noise ratio of the obtained electrocardiogram signal , And the use of the other bio-signal can be determined according to the calculated signal-to-noise ratio.

A method for detecting an arrhythmia according to an exemplary embodiment of the present invention includes the steps of acquiring an electrocardiogram signal, acquiring at least one of a heart trajectory signal and a heart sound signal, and acquiring at least one of the obtained electrocardiogram signal, And / or detecting the arrhythmia using the heart sound signal or using the heart ballistic signal and / or the heart sound signal.

The method and apparatus for detecting arrhythmia according to various embodiments of the present invention can support accurate measurement of arrhythmia occurring to a user.

FIG. 1 shows an example in which an arrhythmia detecting device according to an embodiment of the present invention is attached to a user's body.
2 is a diagram showing waveforms of an electrocardiogram signal and a heart ballistic signal measured through an arrhythmia detection apparatus, according to various embodiments of the present invention.
3 shows a block diagram of an arrhythmia detection apparatus according to an embodiment of the present invention.
4 is a flowchart illustrating an arrhythmia detection method according to an embodiment of the present invention.
5 is a diagram for explaining a signal-to-noise ratio (SNR) calculation of an electrocardiogram signal according to an embodiment of the present invention.
FIG. 6 is a flowchart illustrating a method of detecting an arrhythmia using a cardiovascular signal according to an embodiment of the present invention. Referring to FIG.
7 is a view for explaining threshold designation of an electrocardiogram signal according to an embodiment of the present invention.
FIG. 8 is a diagram for explaining a method of detecting an R peak point of an electrocardiogram signal using a J peak point of a cardiovascular signal according to an embodiment of the present invention. Referring to FIG.
9 is a flowchart illustrating a method of detecting an arrhythmia using a cardiac tropon signal according to another embodiment of the present invention.
10 is a view for explaining threshold designation of an electrocardiogram signal according to an embodiment of the present invention.
11 is a flowchart illustrating a method for detecting an arrhythmia using a heart sound signal according to another embodiment of the present invention.
12 is a diagram for explaining a method of detecting an R peak point of an electrocardiogram signal using minutiae points of a heart sound signal according to an embodiment of the present invention.
FIG. 13 is a flowchart illustrating a method of detecting an arrhythmia using a heart sound signal according to another embodiment of the present invention.
14 is a flowchart of a method of outputting an arrhythmia risk alert according to an embodiment of the present invention. FIG. 15 is an exemplary diagram illustrating an arrhythmia risk output from an external electronic device and an alert for reducing arrhythmia risk according to an embodiment of the present invention. FIG.
16 is a flowchart illustrating a method of storing an individual arrhythmogenic factor according to an embodiment of the present invention.
17 is a flowchart illustrating a method of informing the risk of an arrhythmia and the reduction of an arrhythmia according to an embodiment of the present invention.
FIG. 18 is a diagram illustrating an arrhythmia risk and an arrhythmia reduction notification according to an embodiment of the present invention. FIG.
FIG. 19 is a flowchart for explaining a method of outputting a reattachment notification when there is an error in the attachment of an arrhythmia detection device, according to an embodiment of the present invention. FIG.
FIG. 20 is an exemplary diagram of an electrocardiogram signal measured when an error in the attachment of the arrhythmia detecting device is detected according to an embodiment of the present invention. FIG.
FIG. 21 is an exemplary view for explaining a method of outputting a reattachment notification when there is an error in the attachment of an arrhythmia detection device, according to an embodiment of the present invention. FIG.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the following embodiments of the present invention are only for embodying the present invention and do not limit or limit the scope of the present invention. It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

The terms "first "," second ", and the like can be used to describe various elements, but the elements should not be limited by terms. Terms are used only for the purpose of distinguishing one component from another.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. While the terms used throughout the specification have taken into account their function, they have chosen the general terms which are currently widely used, but may vary depending on the intentions, precedents, or emergence of new techniques employed by those skilled in the art. In addition, in certain cases, there are some terms selected arbitrarily by the applicant. In this case, the meaning of the selected term will be described in detail. Therefore, terms used throughout the specification should be defined based on the meaning of the term and the contents throughout the specification, not on the name of a simple term.

The singular expressions include plural expressions unless the context clearly dictates otherwise. It is to be understood that the terms "comprises", "having", and the like in the specification are intended to specify the presence of stated features, integers, steps, operations, elements, parts or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, parts, or combinations thereof. In particular, the numbers set forth in the specification are intended to be illustrative, and the invention should not be limited by the numbers set forth in the specification.

FIG. 1 shows an example in which an arrhythmia detecting apparatus 100 according to an embodiment of the present invention is attached to a user's body.

2 is a diagram showing waveforms of an electrocardiogram signal and a heart ballistic signal measured by the arrhythmia detecting apparatus 100 according to various embodiments of the present invention.

Referring to FIGS. 1 and 2, the arrhythmia detecting apparatus 100 may be attached to a user's body to detect an arrhythmia of a user, as shown in FIG. For example, the arrhythmia detection device 100 may be attached within a certain distance around a site (e.g., a chest) where a heart of a user is located. Arrhythmia detection may include determining at least one of the presence or absence of arrhythmia, and the type of arrhythmia that has occurred. The types of arrhythmia may include tachycardia, heart rate is much faster than normal, bradycardia is much slower than normal, and exaggerated contractions out of order. In one embodiment, the arrhythmia detection device 100 may include an electrocardiogram (ECG) sensor as well as other biological sensors such as a ballistocardiogram (BCG) sensor and a phonocardiogram (PCG) sensor.

In one embodiment, the arrhythmia detection apparatus 100 detects that the electrocardiogram signal measured through the electrocardiogram sensor 151 contains a small amount of noise (for example, when the signal to noise ratio of the electrocardiogram signal is less than a threshold Or more), or when the waveform of the electrocardiogram signal is measured without distortion, the arrhythmia can be detected using only the electrocardiogram signal.

For example, as shown in 201-1 of FIG. 2A, when the ECG signal is measured without distortion (or when the ECG signal contains a small amount of noise), an arrhythmia is detected using only the electrocardiogram signal . In other words, arrhythmia can be detected using only the ECG signal of 201-1 even when the BCG signal of 201-2 is measured without distortion (or when the heart trajectory signal contains less noise) .

In another example, the electrocardiogram signal can be measured without distortion even when the arrhythmia detecting device 100 is spaced a certain distance from the part of the user's body where the heart is located, as shown in 205-1 of FIG. 2E. For example, although a fluctuation occurs in a base line of an electrocardiogram signal, an R peak point can be clearly measured as a characteristic point of an electrocardiogram signal, and the arrhythmia detecting apparatus 100 extracts an R peak point, Arrhythmia can be detected based on the R peak point. On the other hand, if the arrhythmia detecting apparatus 100 is spaced apart from a part of the user's body where the heart is located, the waveform of the measured heart-shape signal may be distorted. In this case, the arrhythmia detecting apparatus 100 can detect the arrhythmia using only the electrocardiogram signal. However, the present invention is not limited to this. Even when the electrocardiogram signal contains a small amount of noise, an arrhythmia can be detected by using another electrocardiogram signal as well as other biological signals (e.g., cardiac trajectory or cardiac signal).

In another embodiment, the arrhythmia detection apparatus 100 detects that the electrocardiogram signal received through the electrocardiogram sensor 151 includes a large amount of noise (for example, when the signal to noise ratio of the electrocardiogram signal exceeds a threshold , Or when the waveform of the electrocardiogram signal is distorted, an arrhythmia can be detected using a heart ballistic signal or the like. For example, the arrhythmia detection apparatus 100 may detect an arrhythmia based on a cardiac traction signal instead of an electrocardiogram signal when the electrocardiogram signal received through the electrocardiogram sensor 151 includes a lot of noise.

For example, when the user applies a force to a body part to which the arrhythmia detection device 100 is attached, an electromyography signal as noise may be included in the electrocardiogram signal. For example, as shown in 202-1 of FIG. 2B, when the user applies a force to a body part to which the arrhythmia detection device 100 is attached in the time interval A, the waveform of the electrocardiogram signal is distorted by the EMG signal . For example, the magnitude of the electrocardiogram signal can be measured irregularly in the A time interval. In another example, the period at which the peak of the ECG signal appears in the A time interval may be irregular. On the other hand, as shown in 202-2 of FIG. 2B, when the user gives a force on the portion to which the arrhythmogenic detection device 100 is attached, the cardiac tidal signal can be measured without distortion. In this case, the arrhythmia detecting apparatus 100 can detect the arrhythmia using the cardiovascular signal.

In another example, when the arrhythmia detecting apparatus 100 is not in contact with the user's body, for example, when the electrode for measuring the electrocardiogram signal is not in contact with the user's body, Likewise, the waveform of the electrocardiogram signal to be measured may be distorted. For example, the waveform of the electrocardiogram signal to be measured may contain only high frequency components or may be irregular. On the other hand, as shown in 203-2 of FIG. 2C, the waveform of the measured cardiac traction signal can be normally measured without distortion.

In another embodiment, when the arrhythmia detection device 100 is different in the direction in which the user's body is attached in the specified direction, the upper and lower positions of the waveform of the measured electrocardiogram signal are inverted, as shown in 204-1 of Fig. 2D . For example, the point corresponding to the R peak point of the electrocardiogram signal appears as an inverted waveform compared to the normally measured electrocardiogram signal, so that the R peak point may not be detected when the conventional electrocardiogram processing algorithm is applied. On the other hand, when the direction of the arrhythmia detecting device 100 attached to the user's body is different from the designated direction, as shown in 204-2 of FIG. 2D, the cardiac traction signal can be normally measured without distortion.

2 illustrates the arrhythmia detection using the cardiac trajectory signal when the waveform of the electrocardiogram signal received through the arrhythmia detection device 100 is distorted, but the present invention is not limited thereto. For example, when the waveform of the electrocardiogram signal received through the arrhythmia detecting apparatus 100 is distorted, the heart sound sensor 155 may be used to detect the arrhythmia.

In another embodiment, when the waveform of the electrocardiogram signal is distorted, or when the magnitude of the signal-to-noise ratio of the electrocardiogram signal is less than or equal to a specified threshold value, the arrhythmia detection apparatus 100 detects an electrocardiogram signal, Arrhythmia can be detected using heart sound signals. For example, the arrhythmia detection apparatus 100 can extract a feature point (or a feature section) of an electrocardiogram signal waveform using a heart ballistic signal or a heart sound signal. In another example, the arrhythmia detection apparatus 100 may recover at least a part of the electrocardiogram signal waveform using a heart ballistic signal or a heart sound signal.

In one embodiment, the arrhythmia detection apparatus 100 can more accurately detect an arrhythmia based on various biometric information or external environment information, including an electrocardiogram signal, a heart ballistic signal, and a heart sound signal. For example, the arrhythmia detection apparatus 100 may further acquire biometric information such as a blood pressure of a user, a sleep state of a user, a stress level (or index), a hydration of a user's biotissue, or exercise information, The signal, heart ballistic signal, and cardiac signal can be combined with acquired biometric information to more accurately detect arrhythmia. In another example, the arrhythmia detection apparatus 100 can acquire more peripheral information such as temperature, humidity, and the like, and can more accurately detect the arrhythmia by combining the electrocardiogram signal, heart ballistic signal, and cardiac sound signal with acquired peripheral information.

In another embodiment, arrhythmia detection device 100 may determine an individual arrhythmogenic factor. For example, when an arrhythmia is detected based on an electrocardiogram signal or the like, the arrhythmia detection device 100 may detect an arrhythmia-inducible factor such as caffeine, drinking, smoking, stress, blood pressure, Can be measured. The arrhythmia detection device 100 can analyze the correlation between the arrhythmia detection and the arrhythmia-inducible factor. For example, when the arrhythmia-inducible factor is simultaneously measured during the arrhythmia detection for a certain period of time, it is determined that the correlation between the arrhythmia detection and the measured arrhythmia-inducible factor is high, The triggering factor can be determined as an individual's arrhythmogenic agent. The arrhythmia detection apparatus 100 may store information on the determined arrhythmogenic factor of the user in the storage unit 160. [

In one embodiment, when the user's individual arrhythmogenic factor is measured (or detected) again, the arrhythmia detection device 100 may inform the user of the risk (or risk) of the arrhythmia occurrence. In another embodiment, when the arrhythmogenic factor is measured, the arrhythmia detection device 100 may provide a notification (or guide) to reduce the arrhythmia to the user.

FIG. 3 shows a block diagram of an arrhythmia detection apparatus 100 according to an embodiment of the present invention.

3, the arrhythmia detecting apparatus 100 may include a communication unit 110, a sensor unit 120, a storage unit 130, and a controller 140. However, the illustrated components are not essential components. The arrhythmia detection apparatus 100 may be implemented by more components than the illustrated components, and the arrhythmia detection apparatus 100 may be implemented by fewer components.

In one embodiment, the communication unit 110 may include at least one of a wireless LAN, a Bluetooth, and a wired Ethernet in correspondence with the performance and structure of the arrhythmia detection apparatus 100. The communication unit 110 may include a combination of a wireless LAN, Bluetooth, and wired Ethernet.

The communication unit 110 includes a Bluetooth low energy (BLE) communication unit 110, a near field communication unit 110, a WLAN communication unit 110, a Zigbee communication unit 110 An infrared (IR) communication unit 110, a WFD (Wi-Fi Direct) communication unit 110, an UWB (ultra wideband) communication unit 110, and an Ant + communication unit 110, But is not limited thereto.

In one embodiment, the communication unit 110 may connect the arrhythmia detection apparatus 100 with an external device (e.g., a wearable device) under the control of the control unit 140. [ In one embodiment, the control unit 140 may transmit biometric information and external environment information to an external electronic device connected through the communication unit 110. For example, the control unit 140 can transmit biometric information such as an electrocardiogram signal, a heart ballistic signal, and a heart sound signal to an external electronic device through the communication unit 110. In another example, the control unit 140 can transmit external temperature information and external humidity information to the external electronic device through the communication unit 110. [ In one embodiment, under control of the control unit 140, the communication unit 110 may transmit an alert to an external device for the presence or absence of an arrhythmia, the type of arrhythmia generated, or the risk of an arrhythmia and an arrhythmia risk reduction. In another embodiment, under the control of the control unit 170, the communication unit 110 may transmit a notification for reattachment to an external device when an attachment error of the arrhythmia detecting device 100 occurs.

In one embodiment, the sensor unit 120 may include an electrocardiographic sensor 121, a heart ballistic sensor 123, a heart sound sensor 125, and an acceleration sensor 127.

In one embodiment, the electrocardiograph sensor 121 can sense an electrocardiogram (ECG) signal through three electrodes. However, it is not limited thereto. For example, the electrocardiograph sensor 121 may include two electrodes or four or more electrodes.

In one embodiment, the cardiac ballistics sensor 123 may sense a ballistocardiogram (BCG) signal. In one embodiment, the cardiac ballistics sensor 123 may be plural. For example, one cardiac ballistics sensor 123 may be disposed on the inside of the arrhythmia detection device 100 (e.g., on one side of the arrhythmia detection device 100 attached to the user's body when attached to the user's body). The heart ballistic sensor 123 disposed inside the arrhythmia detecting apparatus 100 can sense a heart ballistic signal (hereinafter, referred to as a 'first heart ballistic signal') including noise due to friction between the user's body and the user's clothes have. Another heart ballistic sensor 123 may be disposed outside the arrhythmia detecting apparatus 100 (e.g., one surface exposed to the outside when attached to the user's body). The heart ballistic sensor 123 disposed on the outside of the arrhythmia detecting apparatus 100 detects a heart ballistic signal (hereinafter, referred to as a " heartbeat signal ") containing noises due to friction between the wearer ' Ballistic signal "). In one embodiment, the controller 140 may remove noise by filtering the second cardiac trajectory signal from the first cardiac trajectory signal. In one embodiment, the control unit 140 may include a differential filter for filtering the noise, or an adaptive notch filter.

In one embodiment, the cardiac ballistic sensor 123 may be a load cell, a polyvinylidene fluoride (PVDF) film sensor, an acceleration sensor, or an electromechanical film (EMFi) sensor.

In one embodiment, the phonocardiogram (PCG) 125 can sense the heart sound signal of the user. In one embodiment, the cardiac sound sensor 125 may include a microphone (MIKE) for sensing cardiac sounds.

In one embodiment, the cardiac sound sensor 125 may be multiple. In one embodiment, one heart sound sensor 125 may be disposed inside the arrhythmia detection device 100. [ The heart sound sensor 125 disposed inside the arrhythmia detecting apparatus 100 can sense a heart sound signal including external noise. And the other cardiac sound sensor 125 may be disposed outside the arrhythmia detecting apparatus 100. The heart sound sensor 125 disposed outside the arrhythmia detecting apparatus 100 can sense external noise. The control unit 140 filters the external noise sensed by the heart sound sensor 125 disposed outside the arrhythmia detection apparatus 100 from the heart sound signal detected from the heart sound sensor 125 disposed inside the arrhythmia detection apparatus 100, Noise can be removed.

In one embodiment, the acceleration sensor 127 detects information on the motion of the arrhythmia detection device 100, for example, information on the rotation of the arrhythmia detection device 100, the arrangement state of the arrhythmia detection device 100, and the like . The control unit 140 can measure the movement of the user, the posture of the user, and the like based on the information on the motion of the arrhythmia detection device 100 attached to the user's body. For example, when the acceleration sensor 127 includes the three-axis acceleration sensor 127, data on the X axis, Y axis, and Z axis of the user's body are detected according to the movement of the user, You can measure sudden changes in your body, such as posture (such as sitting, standing, lying down) or physical activity, such as walking, running, or walking, or stunning, falling, or falling.

In one embodiment, although not shown in FIG. 3, the sensor unit 120 may further include a sensor for measuring user biometric information and external environment information. For example, the sensor unit 120 may further include a spectrophotometer. The sensor unit 120 can detect the amount of caffeine in the user's body through a spectrophotometer. In another example, the sensor unit 120 may further include a drinking measurement sensor. The sensor unit 120 can detect the concentration of alcohol in the blood or respiration through the alcohol measurement sensor. In another example, the sensor unit 120 may further include a photorefractive blood flow measurement sensor. The sensor unit 120 can detect the user's sleeping state from the optical blood flow measurement sensor. In another example, the sensor unit 120 may further include an impedance sensor capable of measuring the hydration of the living tissue.

In one embodiment, the sensor unit 120 may further include a temperature sensor for measuring an external temperature and a humidity sensor for measuring humidity to measure external environment information.

However, the sensor unit 120 enumerated above is only an example, and is not limited thereto. For example, the sensor unit 120 may omit some of the sensors listed above, or may further include a sensor.

In one embodiment, the storage unit 130 may store various data or programs for driving and controlling the arrhythmia detection apparatus 100 under the control of the control unit 140. [ In one embodiment, the term "storage unit 130" includes a storage unit 130, a memory card (e.g., micro SD card, USB Memory, not shown). In another example, the storage unit 130 may include a non-volatile memory, a volatile memory, a hard disk drive (HDD), or a solid state drive (SSD).

In one embodiment, the storage 130 may store user-specific arrhythmogenic factors. For example, the controller 140 may determine a factor having a high correlation with the occurrence of arrhythmia as the arrhythmogenic factor among the biometric information or external environment information measured during the detection of the arrhythmia for a predetermined period of time. The storage unit 130 may store the arrhythmogenic factor under the control of the controller 140.

In one embodiment, the control unit 140 may control the overall operation of the arrhythmia detection apparatus 100 and the signal flow between the internal components 110 to 130 of the arrhythmia detection apparatus 100.

In one embodiment, the control unit 140 stores signals or data input from the outside of the arrhythmia detection apparatus 100, or a RAM (RAM) used as a storage area corresponding to various operations performed in the arrhythmia detection apparatus 100. [ A ROM for storing a control program for controlling the arrhythmia detection device 100, and a processor.

Hereinafter, functions performed in the control unit 140 will be described in detail with reference to FIGS. 4 to 21. FIG.

4 is a flowchart illustrating an arrhythmia detection method according to an embodiment of the present invention.

5 is a diagram for explaining calculation of a signal-to-noise ratio (SNR) of an electrocardiogram signal according to an embodiment of the present invention.

Referring to FIG. 4, in step 401, the controller 140 may acquire an electrocardiogram (ECG) signal and other bio-signals.

In one embodiment, the control unit 140 can acquire an electrocardiogram signal from the electrocardiogram sensor 121. [ For example, the control unit 140 may receive the electrocardiogram signal through the three electrodes included in the electrocardiograph sensor 121. [

In one embodiment, the other bio-signal may be a ballistocardiogram (BCG) signal. For example, the control unit 140 may receive a cardiac ballistic signal through the cardiac ballistics sensor 123. In one embodiment, the cardiac ballistic sensor 123 may be a load cell, an acceleration sensor, a polyvinylidene fluoride (PVDF) film sensor, or an electromechanical film (EMFi) sensor. In one embodiment, a plurality of heart ballistic sensors 123 may be included in the arrhythmia detection device 100. For example, one cardiac ballistics sensor 123 may be disposed on the inside of the arrhythmia detection device 100 (e.g., on one side of the arrhythmia detection device 100 attached to the user's body when attached to the user's body). The heart ballistic sensor 123 disposed inside the arrhythmia detecting apparatus 100 can sense a heart ballistic signal (hereinafter, referred to as a 'first heart ballistic signal') including noise due to friction between the user's body and the user's clothes have. Another heart ballistic sensor 123 may be disposed outside the arrhythmia detecting apparatus 100 (e.g., one surface exposed to the outside when attached to the user's body). The heart ballistic sensor 123 disposed on the outside of the arrhythmia detecting apparatus 100 detects a heart ballistic signal (hereinafter, referred to as a " heartbeat signal ") containing noises due to friction between the wearer ' Ballistic signal "). The control unit 140 can remove the noise by filtering the second cardiac tidal ballistic signal (i.e., noise) from the first cardiac tidal signal. In one embodiment, the control unit 140 may include a differential filter for filtering the noise, or an adaptive notch filter. In another embodiment, a differential filter or an adaptive notch filter may be included in the arrhythmia detection device 100, as an alternative to the control unit 140. [

In another embodiment, the other bio-signal may be a phonocardiogram (PCG) signal. For example, the control unit 140 may receive the heart sound signal from the heart sound sensor 125. [ In one embodiment, the cardiac sound sensor 125 may include a microphone (MIKE) for sensing cardiac sounds. In one embodiment, a plurality of heart sound sensors 125 may be included in the arrhythmia detection device 100. In one embodiment, one heart sound sensor 125 may be disposed inside the arrhythmia detection device 100. [ The heart sound sensor 125 disposed inside the arrhythmia detecting apparatus 100 can sense a heart sound signal including external noise. And the other cardiac sound sensor 125 may be disposed outside the arrhythmia detecting apparatus 100. The heart sound sensor 125 disposed outside the arrhythmia detecting apparatus 100 can sense external noise. The control unit 140 filters the external noise sensed by the heart sound sensor 125 disposed outside the arrhythmia detection apparatus 100 from the heart sound signal detected from the heart sound sensor 125 disposed inside the arrhythmia detection apparatus 100, Noise can be removed.

In Fig. 4, a heart trajectory signal or a heart sound signal is exemplified as another living body signal, but the present invention is not limited thereto.

In one embodiment, the control unit 140 may amplify the acquired electrocardiogram signal, heart ballistic signal, or cardiac sound signal. For example, the control unit 140 may include an amplification module for amplifying the obtained electrocardiogram signal and heart ballistic signal, or cardiac sound signal. In another example, the arrhythmia detection apparatus 100 may include a module for amplifying an electrocardiogram signal, a heart trajectory signal, or a heart sound signal as a separate structure from the control unit 140.

In one embodiment, the control unit 140 may digitize the acquired electrocardiogram signal, cardiac trajectory signal, or cardiac sound signal. For example, the control unit 140 may include an analog-to-digital converter (ADC) for digitizing the acquired electrocardiogram signal, heart ballistic signal, or cardiac sound signal. In another example, the ADC may be included in the arrhythmia detection apparatus 100 as a separate structure from the control unit 140. [

In one embodiment, the control unit 140 may perform processing on the digitized electrocardiogram signal, the heart ballistic signal, or the heart sound signal. For example, the control unit 140 may perform a digital processing of the electrocardiogram signal, the heart ballistic signal, or the heart sound signal through digital operation processes (e.g., FFT (Fast Fourier Transform) operation, differential operation, . In an embodiment, the digital signal processing module may be included in the arrhythmia detection apparatus 100 as a separate configuration from the control unit 140. [

In one embodiment, the control unit 140 may filter the electrocardiogram signal and the heart ballistic signal, or the noise included in the cardiac signal. For example, the control unit 140 may remove the baseline drift fluctuation included in the electrocardiogram signal, the heart ballistic signal, or the heart sound signal. The control unit 140 may include a high pass filter (HPF) for eliminating baseline variations. In another embodiment, it may comprise a low pass filter (LPF) for removing noise contained in the obtained signal.

In step 403, the control unit 140 may calculate a signal-to-noise ratio (SNR) of the obtained electrocardiogram signal. Hereinafter, the SNR calculation of the electrocardiogram signal will be described with reference to FIG.

5 is a diagram for explaining a signal-to-noise ratio (SNR) calculation of an electrocardiogram signal according to an embodiment of the present invention. The signal-to-noise ratio represents the ratio of the relative magnitude of the signal to the noise. The smaller the signal-to-noise ratio is, the more noise is included in the measured signal.

In FIG. 5, the horizontal axis represents the frequency and the vertical axis represents the spectral profile, that is, the magnitude of the electrocardiogram signal. In one embodiment, the harmonic components of the measured electrocardiogram signal correspond to the signal, and the components between the harmonic components may correspond to noise. For example, the area obtained by integrating the harmonic components in FIG. 5, for example, the area corresponding to A, may correspond to the signal. The area where the components between the harmonic components are integrated, for example, the area corresponding to B may correspond to the noise. In one embodiment, the control unit 140 may calculate the ratio of the partial area corresponding to A to the partial area corresponding to B, and calculate the calculated ratio as the signal-to-noise ratio (SNR). However, the signal-to-noise ratio calculation illustrated in FIG. 5 is an example, and is not limited thereto.

Returning to FIG. 4, in step 405, the control unit 140 may compare the signal-to-noise ratio (SNR) of the electrocardiogram signal with the specified threshold value. In one embodiment, the threshold may be specified differently at design time, depending on the intent of the designer. In one embodiment, the control unit 140 may designate different threshold values according to the hardware configuration of the arrhythmia detecting apparatus 100. [ For example, the control unit 140 may designate different thresholds according to the type of electrodes of the electrocardiogram sensor 121 included in the arrhythmia detecting apparatus 100. In another example, the control unit 140 may designate different threshold values in consideration of the position where the arrhythmia detecting apparatus 100 is attached to the user's body, and the like. In another embodiment, the control unit 140 may specify the threshold value differently from the measurement target, for example, the user. However, the threshold value designation is not limited thereto.

In step 407, the controller 140 may determine whether to use another bio-signal for detecting the arrhythmia according to the signal-to-noise ratio of the electrocardiogram signal. In one embodiment, when the signal-to-noise ratio of the electrocardiogram signal is less than or equal to the specified threshold value, the noise ratio included in the electrocardiogram signal may be high. If the noise ratio included in the electrocardiogram signal is high, the waveform of the electrocardiogram signal may be distorted and it may be difficult to measure the accurate electrocardiogram. When the signal-to-noise ratio of the electrocardiogram signal is equal to or less than a predetermined threshold value, the controller 140 can detect an arrhythmia using the obtained other bio-signal.

In one embodiment, the controller 140 may recover at least a portion of the electrocardiogram signal (or the waveform of the electrocardiogram signal) using the cardiac tidal signal or the cardiac signal. The control unit 140 can detect an arrhythmia based on the restored electrocardiogram signal. In another embodiment, the control unit 140 may detect an arrhythmia based on a cardiac traction signal or a heart sound signal instead of an electrocardiogram signal. A method of detecting an arrhythmia based on a restored electrocardiogram signal and a method of detecting an arrhythmia based on a heart valve signal or a heart sound signal instead of an electrocardiogram signal will be described in detail with reference to FIG.

In one embodiment, if the signal-to-noise ratio of the electrocardiogram signal exceeds a specified threshold, the controller 140 may detect an arrhythmia based on the electrocardiogram signal. However, the present invention is not limited to this, and even when the signal-to-noise ratio of the electrocardiogram signal exceeds the specified threshold value, arrhythmia can be detected using other acquired bio-signals. In one embodiment, if the signal-to-noise ratio of the electrocardiogram signal exceeds a specified threshold value, the noise ratio included in the electrocardiogram signal may be low. When the noise ratio included in the electrocardiogram signal is low, the waveform of the electrocardiogram signal can be clearly measured without distortion. In one embodiment, the control unit 140 may extract feature points (or feature regions) from the electrocardiogram signal. In one embodiment, the control unit 140 may calculate information including the heartbeat number and the like based on the extracted feature points. In one embodiment, the control unit 140 can recognize the presence or absence of an arrhythmia based on the calculated information. In one embodiment, various algorithms can be used to recognize the arrhythmia and arrhythmia types. For example, a Hidden Markov Model-based algorithm and a Heart best matrix-based algorithm can be used to recognize arrhythmia and arrhythmia types. However, the algorithm used to recognize the presence or absence of arrhythmia and the arrhythmia type is not limited thereto.

FIG. 6 is a flowchart illustrating an arrhythmia detection method using a heart ballistic signal according to an embodiment of the present invention. Referring to FIG.

Referring to FIG. 6, in step 601, the controller 140 may acquire an electrocardiogram signal and a heart ballistic signal. In step 603, the controller 140 may calculate a signal-to-noise ratio (SNR) of the electrocardiogram signal obtained in step 601. Since the processes 601 and 603 are overlapped with the processes 401 and 403, a detailed description will be omitted.

In step 605, the controller 140 may calculate the signal-to-noise ratio of the cardiac trajectory signal. For example, the controller 140 may analyze the waveform of the cardiac traction signal (or the pattern of the cardiac traction signal) in the frequency domain of the cardiac traction signal. The controller 140 can calculate the signal-to-noise ratio of the cardiac trajectory signal by calculating the ratio of the area corresponding to the harmonic components to the area corresponding to the harmonic components in the pattern of the cardiac trajectory signal.

In one embodiment, although not shown in FIG. 6, if the signal-to-noise ratio of the cardiac trajectory signal exceeds the specified threshold, the process may proceed to step 607. In another embodiment, if the signal to noise ratio of the cardiac tidal signal is below a specified threshold, the controller 140 may detect an arrhythmia based solely on the electrocardiogram signal, or may acquire the cardiac tidal signal again.

In one embodiment, in FIG. 6, in step 603, the control unit 140 calculates the signal-to-noise ratio of the electrocardiogram signal and calculates the signal-to-noise ratio of the cardiac tidal signal. For example, if the signal-to-noise ratio of the electrocardiogram signal is less than the designated threshold value in step 609, the controller 140 does not calculate the signal-to-noise ratio of the cardiac traction signal in step 605, can do.

In one embodiment, the process for calculating the signal-to-noise ratio of the cardiac trajectory signal is performed between steps 609 and 611, and if the calculated signal-to-noise ratio of the cardiac trough signal is less than or equal to the specified threshold value, The ECG signal and the heart ballistic signal can be obtained again. For example, if the signal-to-noise ratio of the electrocardiogram signal is below a threshold value in step 609 and the signal-to-noise ratio of the calculated cardiac trajectory signal is also below the threshold value, if both the electrocardiogram signal and the heart- . In this case, the controller 140 may again acquire the electrocardiogram signal and the heart ballistic signal from the electrocardiogram sensor 121 and the heart ballistic sensor 123. In another embodiment, a process for calculating the signal-to-noise ratio of the cardiac trajectory signal is performed between steps 609 and 611, and if the calculated signal-to-noise ratio of the cardiac trough signal exceeds the specified threshold, step 611 Can proceed.

In step 607, the control unit 140 may compare the signal-to-noise ratio of the electrocardiogram signal and the specified threshold value. With reference to the threshold designation, it will be described in detail with reference to FIG.

7 is a view for explaining threshold designation of an electrocardiogram signal according to an embodiment of the present invention.

Referring to FIG. 7, reference numeral 701 is a graph showing an electrocardiogram signal having a relatively high signal-to-noise ratio (that is, an electrocardiogram signal having a signal-to-noise ratio exceeding a threshold value) in a time domain and a frequency domain. 703 is a graph showing an electrocardiogram signal having a relatively low signal-to-noise ratio (i.e., an electrocardiogram signal having a signal-to-noise ratio lower than a threshold value) in the time domain and the frequency domain.

Comparing 701 and 703, in one embodiment, the waveform of the electrocardiogram signal at 701 can be measured clearly without distortion. For example, feature points corresponding to the R peak points 711, 712, and 713, including the QRS group of the electrocardiogram signal, can be measured regularly and clearly. In another embodiment, in an electrocardiogram signal of 703, a greater number of voltage peak points 714 to 719 can be measured than an electrocardiogram signal of 701. [ In one embodiment, the voltage peak point measured at 703 may be the R peak point 714, 716, 718 of the electrocardiogram signal, or the peak point 715, 717, 719 generated by the noise.

In one embodiment, the control unit 140 can designate the threshold value to distinguish the electrocardiogram signal waveform of 701 and the electrocardiogram signal waveform of 703. For example, the control unit 140 may convert the threshold value into a signal-to-noise ratio in an electrocardiogram signal waveform in which the magnitude of the voltage peak corresponding to the R peak point of the electrocardiogram signal is measured to be larger than the magnitude of the voltage peak point corresponding to the noise, .

In another example, the control unit 140 can analyze the spectrum of the electrocardiogram signal measured in the frequency domain to specify a threshold value. For example, when comparing 701 and 703, the number of harmonic (harmonic or harmonic) components of an electrocardiogram signal can be measured from 701 to 8, and from 703 to 3. The controller 140 may designate a threshold according to the number of harmonic components of the electrocardiogram signal. For example, in order to distinguish the electrocardiogram signal waveform of 701 and the electrocardiogram signal waveform of 703, the controller 140 may designate the signal-to-noise ratio of the electrocardiogram signal measured by the number of harmonic components of the electrocardiogram signal as a threshold value have.

In one embodiment, the control unit 140 may designate different threshold values according to the hardware configuration of the arrhythmia detecting apparatus 100. [ For example, the control unit 140 may designate different thresholds according to the type of electrodes of the electrocardiogram sensor 121 included in the arrhythmia detecting apparatus 100. In another example, the control unit 140 may designate different threshold values in consideration of the position where the arrhythmia detecting apparatus 100 is attached to the user's body, and the like. In another embodiment, the control unit 140 may specify the threshold value differently from the measurement target, for example, the user. However, the threshold value designation is not limited thereto.

6, if the controller 140 determines in step 609 that the signal-to-noise ratio of the electrocardiogram signal is less than or equal to the predetermined threshold value, the controller 140 determines in step 611 whether the electrocardiogram signal has the J- R peak point can be detected. Hereinafter, a method of detecting the R peak point of the electrocardiogram signal using the J peak point of the cardiovascular signal will be described in detail with reference to FIGS. 7 and 8. FIG.

FIG. 8 is a diagram for explaining a method of detecting an R peak point of an electrocardiogram signal using a J peak point of a cardiovascular signal according to an embodiment of the present invention. Referring to FIG. 8 is a diagram showing a correlation between an electrocardiogram signal and a heart ballistic signal.

7 and 8, reference numeral 801 denotes a waveform of an electrocardiogram signal, and reference numeral 803 denotes a heart ballistic signal waveform measured at the same time as a waveform of an electrocardiogram 801. FIG. 801 and 803, the time value corresponding to the R peak point of the electrocardiogram signal and the J peak point of the cardiac trajectory signal, that is, the R-J interval (d), may be a constant value.

In one embodiment, the control unit 140 may specify a fixed time range (or windowing range) to detect the R peak point of the electrocardiogram signal from the J peak point 811 of the cardiac trajectory signal. For example, the control unit 140 may designate a time range from the J peak point of the cardiac trajectory signal to the g point of the cardiac trajectory signal as a predetermined time range. In another example, the control unit 140 may designate the time range from the J peak point of the cardiac trajectory signal to the a1 point of the cardiac trajectory signal as a certain time range. However, the specified time range is not limited thereto. For example, the control unit 140 may designate a time range from the J peak point to the j point, i point, or h point as a certain time range. In another example, a time range may be specified as an averaged value by a statistic, or a time range designation that reflects demographic information (e.g., gender, age, race, etc.), or based on user (or wearer) By analyzing the time range, for example, electrocardiogram and heart ballistic signal, it is possible to specify the time range differently for each individual by reflecting the average value and the minimum / maximum value of the RJ interval measurement values. However, it is not limited thereto.

In one embodiment, the control unit 140 may detect an R peak point of the ECG signal within a predetermined time range from the J peak point of the cardiovascular signal. For example, in an electrocardiogram signal measured at a time domain of 703, it can be assumed that a J peak point of a cardiac trajectory signal is detected at a time of 100 ms and 500 ms, and a certain time range is designated as a time range of 100 ms. The control unit 140 detects the voltage peak point 714 and the voltage peak point 716 of the electrocardiogram signal measured within the 100 ms time range from the J peak point of 100 ms and 500 ms time of the cardiac traction signal as the R peak point of the electrocardiogram signal can do. In another embodiment, the control unit 140 may detect the voltage peak point 715 measured between the voltage peak point 714 and the voltage peak point 716 as a voltage peak point generated by the noise.

Returning to FIG. 6, in step 613, the control unit 140 may extract the feature of the electrocardiogram signal based on the R peak point of the electrocardiogram signal. For example, the control unit 140 may extract the R-peak point distance of the electrocardiogram signal, that is, the R-R interval. However, the characteristics of the electrocardiogram signal are not limited to the R-R interval. For example, the control unit 140 may extract the PR interval, the QT interval, the length of the ST segment, and the TP segment from the electrocardiogram signal.

In step 615, the control unit 140 can detect the arrhythmia using the extracted features. For example, the control unit 140 may generate information on the heartbeat number based on the extracted features. In one embodiment, the control unit 140 can recognize the presence or absence of an arrhythmia based on the generated information. In one embodiment, various algorithms can be used to recognize the arrhythmia and arrhythmia types. For example, a Hidden Markov Model-based algorithm and a Heart best matrix-based algorithm can be used to recognize arrhythmia and arrhythmia types. However, the algorithm used to recognize the presence or absence of arrhythmia and the arrhythmia type is not limited thereto.

In step 617, if the signal-to-noise ratio of the electrocardiogram signal exceeds a predetermined threshold value in step 609, the controller 140 may detect the arrhythmia based on the electrocardiogram signal alone without using the cardiac tidal signal. For example, the controller 140 may extract characteristics such as R-R interval, ie, R-R interval, of the electrocardiogram signal, and generate information on the heart rate based on the extracted characteristics. The control unit 140 can recognize the presence or absence of the arrhythmia based on the generated information.

9 is a flowchart illustrating a method of detecting an arrhythmia using a cardiac tropon signal according to another embodiment of the present invention.

Referring to FIG. 9, in step 901, the control unit 140 may acquire an electrocardiogram signal and a heart ballistic signal. In step 903, the control unit 140 may calculate the signal-to-noise ratio of the electrocardiogram signal. In step 905, the control unit 140 may calculate the signal-to-noise ratio of the cardiac trajectory signal. Since the processes 901 to 905 are the same as the processes 601 to 605 of FIG. 6, a detailed description will be omitted.

In step 907, the control unit 140 may compare the signal-to-noise ratio of the electrocardiogram signal with the first threshold value and the second threshold value. With respect to the threshold value designation, it will be described in detail with reference to FIG.

10 is a view for explaining threshold designation of an electrocardiogram signal according to an embodiment of the present invention.

Referring to FIG. 10, reference numeral 1001 denotes a graph showing an electrocardiogram signal having a relatively high signal-to-noise ratio (that is, an electrocardiogram signal having a signal-to-noise ratio exceeding a threshold value) in a time domain and a frequency domain. 1003 is a graph showing an electrocardiogram signal having a relatively low signal-to-noise ratio in the time domain and the frequency domain. 1005 is a graph showing an electrocardiogram signal having a very low signal-to-noise ratio of the electrocardiogram signal in the time domain and the frequency domain. In one embodiment, 1001 and 1003 may be the same as 701 and 703 in FIG. In another embodiment, comparing 1003 and 1005, it can be said that the signal-to-noise ratio of the electrocardiogram signal shown at 1005 is lower than the signal-to-noise ratio of the electrocardiogram signal shown at 1003. For example, in the frequency domain of 1005, the frequency range of about 7 Hz to 9 Hz of the electrocardiogram signal corresponds to the range of the signal, while the range of 0 Hz to 7 Hz and the range of 9 Hz or more may correspond to the noise range. In another example, comparing 1001 and 1005, the peak points of the voltage in the time domain of 1005 can be measured in an irregular, distorted form, while in 1001 the R peak point in the time domain is measured periodically without distortion have.

In one embodiment, the controller 140 may designate a first threshold value and a second threshold value to classify the electrocardiogram signal waveform of 1001, the electrocardiogram signal waveform of 1003, and the electrocardiogram signal waveform of 1005.

For example, when the first threshold value is 1003, the magnitude of the voltage peak corresponding to the R peak point of the electrocardiogram signal is greater than the magnitude of the voltage peak point corresponding to the noise, Signal-to-noise ratio.

In another example, the controller 140 may analyze the spectrum of the electrocardiogram signal measured in the frequency domain to specify a first threshold value. For example, when comparing 1001 to 1005, the number of harmonic (harmonic or harmonic) components of an electrocardiogram signal may be measured from 1001 to 8, from 1003 to 3, and from 1005 to 1. The controller 140 may designate a first threshold value according to the number of harmonic components of the electrocardiogram signal. For example, in order to classify the electrocardiogram signal waveform of 1001 and the electrocardiogram signal waveform of 1003, the controller 140 sets the signal-to-noise ratio of the electrocardiogram signal measured as six harmonic components of the electrocardiogram signal as a first threshold value Can be specified.

In another example, the controller 140 may specify a second threshold value by analyzing the number of voltage peaks in a specified time interval in the time domain. For example, the number of R peak points of an electrocardiogram signal can be measured as three in a time interval of 1001. During the same time period as the time period of 1001, the number of voltage peak points at 1003 is measured as six, and at 1005, the number of voltage peak points can be measured as nine. The controller 140 may designate the signal-to-noise ratio in the electrocardiogram signal corresponding to the number of the voltage peak points as the second threshold value for the same time period as the time period 1001. [

In another example, the controller 140 may analyze the spectrum of the electrocardiogram signal measured in the frequency domain to specify a second threshold value. For example, when comparing 1001 to 1005, the number of harmonic (harmonic or harmonic) components of an electrocardiogram signal may be measured from 1001 to 8, from 1003 to 3, and from 1005 to 1. The controller 140 may designate a second threshold value according to the number of harmonic components of the electrocardiogram signal. For example, in order to classify the electrocardiogram signal waveform of 1003 and the electrocardiogram signal waveform of 1005, the controller 140 sets the signal-to-noise ratio of the electrocardiogram signal measured with the number of harmonic components of the electrocardiogram signal to a second threshold value Can be specified.

In one embodiment, the controller 140 may specify the first threshold value and the second threshold value differently according to the hardware configuration of the arrhythmia detection apparatus 100. [ For example, the controller 140 may designate the first threshold value and the second threshold value differently according to the type of the electrode of the electrocardiogram sensor 121 included in the arrhythmia detection apparatus 100. [ In another example, the control unit 140 may designate the first threshold value and the second threshold value differently in consideration of the position or the like where the arrhythmia detecting apparatus 100 is attached to the user's body. In another embodiment, the control unit 140 may designate the first threshold value and the second threshold value differently according to the user to be measured, for example. However, the threshold designation is not limited to the above example.

9, in step 911, when the controller 140 determines in step 909 that the signal-to-noise ratio of the electrocardiogram signal is greater than the first threshold value, the controller 140 detects an arrhythmia based on the electrocardiogram signal . Since the process 911 is the same as the process 411 of FIG. 4 and the process 617 of FIG. 6, a detailed description thereof will be omitted.

If the control unit 140 determines in step 913 that the signal-to-noise ratio of the electrocardiogram signal is less than or equal to the first threshold value and greater than the second threshold value in step 913, the control unit 140 uses the J peak point of the heart- The R peak point of the electrocardiogram signal can be detected. In step 917, the controller 140 may extract the feature of the electrocardiogram signal based on the detected R peak point. In step 919, the control unit 140 can detect an arrhythmia using the extracted features. Since the processes 915 to 919 are the same as the processes 611 to 615 of FIG. 6, a detailed description will be omitted.

In step 921, if the controller 140 determines in step 913 that the signal-to-noise ratio of the electrocardiogram signal is less than or equal to the second threshold value, the controller 140 may extract a feature of the cardiac tidal signal. The control unit 140 can confirm that the waveform of the electrocardiogram signal is distorted to such an extent that it can not extract the feature of the electrocardiogram signal and can extract the feature of the cardiac tidal signal, for example, the feature point (or feature region). For example, the controller 140 may extract the time interval between J peaks of the cardiac trajectory signal, that is, the J-J interval. However, features extracted from the cardiac ballistic signal are not limited thereto.

In step 923, the control unit 140 may detect the arrhythmia using the features extracted from the cardiac trajectory signal. For example, the control unit 140 can calculate the heartbeat number by calculating the number of J peaks of the cardiac traction signal for a predetermined time (e.g., 1 minute). The control unit 140 can detect the presence or absence of an arrhythmia, the type of arrhythmia, or the like based on the calculated heartbeat count. In another example, the controller 140 may calculate the J-J interval of the cardiac trajectory signal, and may detect the arrhythmia type, arrhythmia type, and the like based on the calculated J-J interval. In one embodiment, various algorithms can be used to recognize the arrhythmia and arrhythmia types. For example, a Hidden Markov Model-based algorithm and a Heart best matrix-based algorithm can be used to recognize arrhythmia and arrhythmia types. However, the algorithm used is not limited thereto.

11 is a flowchart illustrating a method for detecting an arrhythmia using a heart sound signal according to another embodiment of the present invention.

Referring to FIG. 11, in step 1101, the controller 140 may acquire an electrocardiogram signal and a heart sound signal. In step 1103, the controller 140 may calculate a signal-to-noise ratio (SNR) of the electrocardiogram signal obtained in step 1101. The process 1101 and the process 1103 are overlapped with the process 401 and the process 403, respectively, and thus a detailed description thereof will be omitted.

In step 1105, the control unit 140 may calculate the signal-to-noise ratio of the heart sound signal. For example, the control unit 140 may analyze the waveform of the heart sound signal (or the pattern of the heart sound signal) in the frequency domain of the heart sound signal. The controller 140 may calculate the signal-to-noise ratio of the heart sound signal by calculating the ratio of the area corresponding to the harmonic components to the area corresponding to the harmonic components in the pattern of the heart sound signal.

In one embodiment, although not shown in FIG. 11, if the signal-to-noise ratio of the cardiac sound signal exceeds the specified threshold, the process may proceed to step 1107. In another embodiment, when the signal-to-noise ratio of the cardiac sound signal is less than or equal to the specified threshold value, the controller 140 may detect the arrhythmia based on the electrocardiogram signal alone or acquire the cardiac sound signal again.

11, the controller 140 calculates the signal-to-noise ratio of the electrocardiogram signal and calculates the signal-to-noise ratio of the heart sound signal in step 1103, but the present invention is not limited thereto. For example, if the signal-to-noise ratio of the electrocardiogram signal is equal to or lower than a predetermined threshold value in step 1109, the controller 140 may calculate the signal-to-noise ratio of the cardiac sound signal in step 1109 without calculating the signal- have.

In one embodiment, the process of calculating the signal-to-noise ratio of the cardiac sound signal is performed between steps 1109 and 1111. If the signal-to-noise ratio of the computed cardiac sound signal is less than or equal to the specified threshold value, And the heart sound signal can be acquired again. For example, if the signal-to-noise ratio of the electrocardiogram signal is less than or equal to the threshold value and the signal-to-noise ratio of the computed cardiac signal is less than the threshold value in step 1109, then both the electrocardiogram signal and the cardiac- . In this case, the controller 140 may acquire electrocardiogram signals and heart sound signals from the electrocardiogram sensor 121 and the heart sound sensor 125 again. In another embodiment, a process for calculating the signal-to-noise ratio of the heart sound signal is performed between steps 1109 and 1111, and if the calculated signal-to-noise ratio of the calculated heart sound signal exceeds the specified threshold value, have.

In step 1107, the control unit 140 may compare the signal-to-noise ratio of the electrocardiogram signal and a specified threshold value. Since the process 1107 is the same as the process 607 of FIG. 6, a detailed description will be omitted.

If the controller 140 determines in step 1111 that the signal-to-noise ratio of the electrocardiogram signal is lower than or equal to the predetermined threshold value, the controller 140 may detect the R peak of the electrocardiogram signal using the designated point of the heart sound signal have. Hereinafter, with reference to FIG. 12, a method of detecting an R peak point of an electrocardiogram signal using minutiae points of a heart sound signal will be described in detail.

12 is a diagram for explaining a method of detecting an R peak point of an electrocardiogram signal using minutiae points of a heart sound signal according to an embodiment of the present invention.

Referring to FIG. 12, FIG. 12 shows an electrocardiogram signal waveform 1201 and a heart sound signal waveform 1203 measured based on the same time axis. In one embodiment, the time interval of the heart sound signal waveform 1203 may include four time intervals during which heart sounds are measured. The S1 time interval may correspond to the time interval at which the atrioventricular valve (mitral valve and tricuspid valve) closes at the beginning of the ventricular systole. In the S1 time interval, the heart sound is low and dull, and the duration of the sound may be long. The frequency during the S1 time interval may be between 57 Hz and 70 Hz per second. The S2 time interval is the time interval during which the aortic valve and the pulmonary valve are closed at the beginning of ventricular dilatation. The heart sound may be high and sharp, and the duration of the sound may be short. The frequency during the S2 time period may be from 90 Hz to 100 Hz per second and may coincide with the end of the T wave of the electrocardiogram signal. The time interval of S3 is the time interval during which the ventricular wall vibrates due to blood flowing from the atrium at the beginning of ventricular dilatation and vice versa. The time interval of S4 may be a time interval during which the heart sound is generated due to the driving force of the blood flowing into the ventricle during the atrial systole.

In one embodiment, the controller 140 may detect the first peak point of the S1 time interval as a feature point of the cardiac sound signal. For example, the control unit 140 measures the frequency of the heart sound signal, and can distinguish the S1 time period to the S4 time period according to the measured frequency. The controller 140 detects the S1 time interval and can detect the first peak point 1211 that is the first one of the peak points in which the magnitude of the pressure of the cardiac sound signal is equal to or larger than the designated size within the S1 time interval.

In one embodiment, the control unit 140 may specify a fixed time range (or windowing range) to detect the R peak point of the electrocardiogram signal from the first peak point 1211 of the cardiac sound signal. For example, the control unit 140 may designate a time interval l1 between points 1213 at which the S4 time interval starts from the first peak point to a predetermined time range. However, the predetermined time range for detecting the R peak point of the electrocardiogram signal is not limited thereto. For example, the controller 140 determines a time interval corresponding to a multiple of the time interval between the first peak point 1211 and the point 1213 at which the S4 time interval starts, as a predetermined time for detecting the R peak point of the electrocardiogram signal It can be specified as a range. In another example, a time range may be specified as an averaged value by a statistic, or a time range designation that reflects demographic information (e.g., gender, age, race, etc.), or based on user (or wearer) The time range can be specified differently for each individual by reflecting the average value and the minimum / maximum value of the R peak and the first peak interval measurement by analyzing the time range, for example, the electrocardiogram and the heart sound signal. However, it is not limited thereto.

In one embodiment, the control unit 140 may detect the R peak point of the electrocardiogram signal within a predetermined time range from the first peak point 1211 of the heart sound signal. For example, referring to FIG. 7, in the electrocardiogram signal measured in the time domain 703 of FIG. 7, the first peak point 1211 of the cardiac sound signal is detected at a time of 100 ms and 500 ms, It can be assumed that the time interval l1 between the points 1213 at which the S4 time interval starts from the first peak point 1211) is set to a time range of 100 ms. The control unit 140 detects the voltage peak point 714 and the voltage peak point 716 of the electrocardiogram signal measured within the 100 ms time range from the first peak point of 100 ms and 500 ms time of the heart sound signal as the R peak point of the electrocardiogram signal can do. In another embodiment, the control unit 140 may detect the voltage peak point 715 measured between the voltage peak point 714 and the voltage peak point 716 as a voltage peak point generated by the noise.

Returning to FIG. 11, in step 1113, the controller 140 can extract the feature of the electrocardiogram signal based on the R peak point of the electrocardiogram signal. For example, the control unit 140 may extract the R-peak point distance of the electrocardiogram signal, that is, the R-R interval. However, the characteristics of the electrocardiogram signal are not limited to the R-R interval. For example, the control unit 140 may extract the PR interval, the QT interval, the length of the ST segment, and the TP segment from the electrocardiogram signal.

In step 1115, the control unit 140 may detect the arrhythmia using the extracted feature. For example, the control unit 140 may generate information on the heartbeat number based on the extracted features. In one embodiment, the control unit 140 can recognize the presence or absence of an arrhythmia based on the generated information. In one embodiment, various algorithms can be used to recognize the arrhythmia and arrhythmia types. For example, a Hidden Markov Model-based algorithm and a Heart best matrix-based algorithm can be used to recognize arrhythmia and arrhythmia types. However, the algorithm used to recognize the presence or absence of arrhythmia and the arrhythmia type is not limited thereto.

In step 1117, when the signal-to-noise ratio of the electrocardiogram signal exceeds the predetermined threshold value in step 1109, the controller 140 can detect the arrhythmia based on the electrocardiogram signal alone without using the heart sound signal. For example, the control unit 140 may extract characteristics such as R-R interval of the R-peak point of the electrocardiogram signal, and generate information on the heart rate based on the extracted characteristic. The control unit 140 can recognize the presence or absence of the arrhythmia based on the generated information.

FIG. 13 is a flowchart illustrating a method of detecting an arrhythmia using a heart sound signal according to another embodiment of the present invention.

Referring to FIG. 13, in step 1301, the controller 140 may acquire an electrocardiogram signal and a heart sound signal. In step 1303, the control unit 140 may calculate the signal-to-noise ratio of the electrocardiogram signal. In step 1305, the controller 140 may calculate a signal-to-noise ratio of the heart sound signal. Since the processes 1301 to 1305 are the same as the processes 1101 to 1105 of FIG. 11, a detailed description thereof will be omitted.

In step 1307, the controller 140 may compare the signal-to-noise ratio of the electrocardiogram signal with the first threshold value and the second threshold value. Since the process 1307 is the same as the process 907 of FIG. 9, a detailed description will be omitted.

In step 1311, if the controller 140 determines in step 1309 that the signal-to-noise ratio of the electrocardiogram signal is greater than the first threshold value, the controller 140 may detect the arrhythmia based on the electrocardiogram signal. Since the process 1311 is the same as the process 1117 of FIG. 11, a detailed description will be omitted.

If the control unit 140 determines in step 1313 that the signal-to-noise ratio of the electrocardiogram signal is less than or equal to the first threshold value and greater than the second threshold value in step 1313, the controller 140 generates the electrocardiogram signal It is possible to detect the R peak point. In step 1317, the controller 140 may extract the feature of the electrocardiogram signal based on the detected R peak point. In step 1319, the control unit 140 can detect the arrhythmia using the extracted features. Since the processes 1315 to 1319 overlap with the processes 1111 to 1115 of FIG. 11, a detailed description will be omitted.

If the controller 140 determines in step 1313 that the signal-to-noise ratio of the electrocardiogram signal is lower than the second threshold value in step 1313, the controller 140 may extract the characteristics of the cardiac sound signal. The characteristic of the cardiac signal may be the time distance between the first peak point of one period and the first peak point of the next period. The first peak may be the first one of the peak points in which the magnitude of the pressure of the cardiac signal within the S1 time interval is equal to or greater than the designated magnitude. However, features extracted from the heart sound signal are not limited thereto. For example, the control unit 140 may measure a point at which the vibration ends in one of the S1 to S4 time intervals during which the heart sound is measured. The control unit 140 can calculate the time distance between the point at which the measured vibration ends and the point at which the vibration ends at the next period.

In step 1323, the control unit 140 may detect the arrhythmia using the feature extracted from the heart sound signal. For example, the control unit 140 calculates the number of first peak points of the heart sound signal measured for a predetermined time (e.g., 1 minute), or the time interval between the first peak points, It is possible to calculate the information including the abnormality of the heartbeat interval.

In one embodiment, the control unit 140 can recognize the presence or absence of an arrhythmia based on the calculated information. In one embodiment, various algorithms can be used to recognize the arrhythmia and arrhythmia types. For example, a Hidden Markov Model-based algorithm and a Heart best matrix-based algorithm can be used to recognize arrhythmia and arrhythmia types. However, the algorithm used is not limited thereto.

4 to 13 may be performed through at least one separate signal processing unit other than the control unit 140. In addition, In another embodiment, the process described in Figures 4-13 may be implemented through a separate programming module (e.g., an application) other than the control unit. In one embodiment, the programming module may be stored in storage 130.

In one embodiment, the controller 140 may store at least one of the electrocardiogram signal, the heart ballistic signal, and the cardiac sound signal in the storage unit 130. For example, the control unit 140 may measure at least one of the electrocardiogram signal, the heart ballistic signal, and the cardiac sound signal in all the time intervals of the electrocardiogram signal, and store the result in the storage unit 130. In another example, the controller 140 may measure at least one of the electrocardiogram signal, the heart valve signal, and the cardiac sound signal in an interval in which the electrocardiogram signal is abnormal (for example, a low signal-to- Lt; / RTI > In yet another embodiment, the controller 140 may measure at least one of the electrocardiogram signal, the cardiac tidal signal, and the cardiac signal at all time intervals of the electrocardiogram signal, stores it in the storage unit 130, It is possible to mark the event in the interval (for example, to display the corresponding point or to display interval time information). In one embodiment, arrhythmia can be detected by analyzing at least one of the electrocardiogram signal stored in the storage unit 130, the heart valve signal, and the heart sound signal, for example, in a hospital.

In one embodiment, the control unit 140 may control the communication unit 110 to transmit a measurement signal to a server (e.g., a cloud server) or an external device (e.g., a smart phone, etc.). For example, the control unit 140 may control the communication unit 110 to transmit at least one of the electrocardiogram signal measured at all time intervals of the electrocardiogram signal, the heart ballistic signal, and the cardiac sound signal. In another example, the control unit 140 controls the communication unit 110 (e.g., the controller 110) to transmit at least one of the electrocardiogram signal measured in the period in which the electrocardiogram signal is abnormal (for example, the period in which the signal to noise ratio is low) Can be controlled. In another embodiment, the control unit 140 transmits at least one of event marking (e.g., displaying a corresponding point or displaying interval time information), measured heart ballistic signal, and cardiac sound signal in an abnormal time period The communication unit 110 can be controlled. In one embodiment, arrhythmia can be detected by analyzing at least one of the electrocardiogram signal received from the arrhythmia detection device 100, the cardiac trajectory signal, and the cardiac sound signal in a server or an external device.

In one embodiment, the control unit 140 may mark an event in an interval in which an arrhythmia is detected after an arrhythmia analysis in the arrhythmia measuring apparatus 100. [ In one embodiment, the controller 140 may store data for at least one of the cardiac trajectory signal and the cardiac signal when there is an anomaly in the electrocardiographic signal. In another embodiment, the control unit 140 may store the ECG signal, the heart trajectory signal, and the cardiac signal data in the storage unit 130 in all time intervals.

14 is a flowchart of a method of outputting an arrhythmia risk alert according to an embodiment of the present invention.

In step 1401, the control unit 140 may acquire biometric information and the like from the sensor unit 120.

For example, the control unit 140 may acquire an electrocardiogram signal. In another example, the control unit may acquire an electrocardiogram signal, a heart trajectory signal, or a heart sound signal. In yet another example, the control may acquire a heart trajectory signal or a heart sound signal.

In one embodiment, the controller 140 may further acquire an ECG signal or cardiac sound signal according to the signal-to-noise ratio of the obtained ECG signal.

In step 1403, the control unit 140 may detect an arrhythmia based on the acquired biometric information.

In one embodiment, the controller 140 may extract a feature point (or a feature section) from at least one of an electrocardiogram signal, a heart ballistic signal, and a heart sound signal in order to detect an arrhythmia based on the acquired biometric information.

In one embodiment, the control unit 140 can calculate the blood pressure or the like based on the acquired biometric information. For example, the control unit 140 may extract an R peak point from the electrocardiogram signal and extract a J peak point from the cardiac trajectory signal. The control unit 140 may extract a time interval between the R peak point and the J peak point, that is, the R-J interval, based on the R peak point and the J peak point. The control unit 140 can calculate the blood pressure using the calculated R-J interval and linear regression analysis. However, the method of calculating the blood pressure is not limited thereto.

In one embodiment, the controller 140 may detect an arrhythmia using a feature extracted from an electrocardiogram signal or the like. For example, the control unit 140 may generate information on the heartbeat number based on the extracted features. In one embodiment, the control unit 140 can recognize the presence or absence of an arrhythmia based on the generated information. In one embodiment, various algorithms can be used to recognize the arrhythmia and arrhythmia types. For example, a Hidden Markov Model-based algorithm and a Heart best matrix-based algorithm can be used to recognize arrhythmia and arrhythmia types. However, the algorithm used to recognize the presence or absence of arrhythmia and the arrhythmia type is not limited thereto.

In step 1405, the control unit 140 may output the arrhythmia risk. For example, when the arrhythmia is detected, the control unit 140 may output a notification of the risk of an arrhythmia.

In one embodiment, the arrhythmia risk can be defined as a degree of risk, such as, for example, LOW / HIGH.

In one embodiment, the control unit 140 can output a high risk of arrhythmia when arrhythmia is detected and the calculated blood pressure corresponds to hypertension. For example, atrial fibrillation (a kind of arrhythmia) can cause the production of thrombosis, which can increase the risk of cerebral infarction, and hypertension can cause atrial fibrillation. If the arrhythmia and hypertensive symptoms are manifested together, the control unit can output a high risk.

In one embodiment, the blood pressure may be calculated (or measured) within the arrhythmia measuring device 100, as described in step 1403. In another embodiment, the blood pressure may be calculated from an external device, for example, a wearable device (e.g., a smart watch or the like), a blood pressure monitor, or the like. In another example, the blood pressure may be received from the outside. For example, information about blood pressure may be received from the cloud. For example, information on the blood pressure measured outside the hospital may be received by the user's portable terminal or arrhythmia detecting device 100 through a PHR (Personal Health Record) server interlocked with the portable terminal of the user . When receiving the measured blood pressure from the outside, the control unit compares the blood pressure calculated in the arrhythmia detecting apparatus 100, and can correct the weight or the like in the calculation formula so as to be close to the actual blood pressure.

In one embodiment, the control unit 140 is configured to control the blood pressure, such as a high stress level other than blood pressure, severe exercise, a high degree of tissue hydration (e.g., when the body is edited due to abnormal cardiac function) Which causes thrombogenesis) can be used to output a risk warning.

In one embodiment, the control unit 140 may control the communication unit to transmit information on the risk of an arrhythmia or the like to an external device. For example, the control unit 140 may control the communication unit 110 to transmit information on the calculated blood pressure, presence of arrhythmia, or type of arrhythmia to an external device (e.g., smart phone, tablet, etc.). In one embodiment, an external device that receives information about an arrhythmia risk may output an arrhythmia risk.

In another embodiment, the control unit 140 may control the configuration of the arrhythmia detection apparatus 100 itself to output the arrhythmia risk through at least one of voice notification, LED flash / color, display, and vibration. For example, the arrhythmia detection apparatus 100 may further include an audio output unit, a light output unit (e.g., an LED, etc.), a display, or a haptic module to output an arrhythmia risk.

FIG. 15 is an exemplary diagram illustrating an arrhythmia risk output from an external electronic device and an alert for reducing arrhythmia risk according to an embodiment of the present invention. FIG.

Referring to FIG. 15, in one embodiment, the external electronic device 1501 may receive data for arrhythmia detection from the arrhythmia detection device 100. FIG. In another embodiment, the external electronic device 1501 may receive data on arrhythmia risk from the arrhythmia detection device 100, and alerts for arrhythmia risk reduction.

In one embodiment, when the external electronic device 1501 confirms the arrhythmia detection and blood pressure abnormality based on the data on the received arrhythmia detection, the external electronic device 1501, as shown in FIG. 15, An arrhythmia was detected, a blood pressure is above a normal reference, and a recommendation 1503 such as "I would recommend diet control and exercise." In another embodiment, when the external electronic device 1501 is detected to be in motion through an acceleration sensor, or when it is determined that the stress level is high, "an arrhythmia has been detected, the blood pressure is above a normal reference level. . "Coaching can be provided to lower blood pressure and reduce arrhythmia in response to circumstances.

16 is a flowchart illustrating a method of storing an individual arrhythmogenic factor according to an embodiment of the present invention.

Referring to FIG. 16, in operation 1601, the controller 140 may acquire biometric information and external environment information when detecting an arrhythmia. Alternatively, the controller 140 may analyze the acquired bio-information and external environment information when the arrhythmia is detected. For example, the control unit 140 may detect an arrhythmia based on biometric information (e.g., an electrocardiogram signal, a heart trajectory signal, a heart sound signal, etc.). The control unit 140 can acquire biometric information (e.g., a stress level, a sleep state, etc.) and external environment information (e.g., temperature, humidity, etc.) when the arrhythmia detection is confirmed.

For example, the control unit 140 may obtain information on the amount of caffeine in the body from a spectrophotometer. In another example, the control unit 140 may obtain information on blood or respiratory alcohol concentration from the alcohol measurement sensor. In another example, the control unit 140 may obtain information on the amount of carbon monoxide in the breath from the smoking measurement sensor. In another example, the control unit 140 may acquire information on the stress level based on the electrocardiogram signal obtained from the electrocardiogram sensor 121. [ In another example, the controller 140 may acquire information on the sleep state of the user from the acceleration sensor 127, the electrocardiogram sensor 121, the photoplethysmography measurement sensor, and the like. For example, the control unit 140 acquires information about the sleeping position from the acceleration sensor 127, acquires information about the sleep quality from the electrocardiogram sensor 121, Can be obtained.

In one embodiment, the control unit 140 may calculate the blood pressure using the obtained electrocardiogram signal and heart ballistic signal. For example, the control unit 140 may extract an R peak point from the electrocardiogram signal and extract a J peak point from the cardiac trajectory signal. The control unit 140 may extract a time interval between the R peak point and the J peak point, that is, the R-J interval, based on the R peak point and the J peak point. The control unit 140 can calculate the blood pressure using the calculated R-J interval and linear regression analysis. However, the method of calculating the blood pressure is not limited thereto.

In one embodiment, the controller 140 may acquire information on the external environment information through the sensor unit 120. [ For example, the controller 140 can acquire information on the movement, posture, and the like of the user from the acceleration sensor 127. In another example, the control unit 140 can acquire information on the temperature of the external environment and the temperature of the user's body through the temperature sensor. In another example, the control unit 140 may obtain information about the humidity of the external environment and the hydration of the user's body tissue.

However, the biometric information and the external environment information acquired by the arrhythmia detecting apparatus 100 are not limited to the examples listed above.

In one embodiment, the control unit 140 can receive biometric information and external environment information measured from the outside. For example, the control unit 140 may receive biometric information and external environment information from an external device, a cloud server, or the like.

In one embodiment, the external device (or server) can measure (or sense) biometric information and external environmental information in a variety of ways. For example, an external device (e.g., portable terminal) can store the life log information of the collected user in an external device, or can be integratedly managed in a server such as S-health.

In the case of caffeine, for example, an external device can receive information about caffeine from an IoT device such as a smart cup that analyzes the components of the beverage.

In another example, the external device can extract payment details related to caffeine-containing foods such as coffee, tea, and chocolate at a coffee shop, a convenience store, etc., in a credit card payment letter or the like. If the caffeine-containing food payment history is extracted, the external device may receive information about the caffeine content of the caffeine-containing food, such as the amount of caffeine, from the server operated by the food-related company. In another example, when the caffeine-containing food payment history is extracted, the external device may output a questionnaire to the user as to whether or not the caffeine-containing food is consumed, for example, in the form of a pop-up window or push notification, Can collect information about

In another example, when the external device receives information from the beacon in the coffee shop or when there is a visit to the coffee shop by GPS information or the like, the external device outputs a questionnaire on whether or not the caffeine-containing food such as coffee / tea is consumed, You can collect information about caffeine by inducing input.

In yet another example, the external device can collect information about caffeine by receiving input from the user as to whether or not the caffeine-containing food, such as coffee / tea, has been ingested.

In the case of drinking, for example, an external device may receive information about drinking from an IoT device, such as a smart cup, that analyzes the ingredients of the internal beverage.

In another example, the external device can extract the payment details related to the alcohol-containing product such as a liquor at a restaurant or a convenience store, for example, in a credit card payment letter or the like. When the payment details of the alcohol-containing product are extracted, the external device can receive information on the alcohol of the alcohol-containing product from which the product details are extracted from the server operated by the relevant product-related company. In another example, when an alcohol-containing product payment history is extracted, the external device outputs a questionnaire to the user as to whether or not the user has consumed the alcohol-containing product, for example, in the form of a pop-up window or push notification, (Or whether they are drinking).

In another example, when an external device receives information from a beacon in a liquor store such as a restaurant or a pub, or when there is a visit to a liquor store by GPS information or the like, a questionnaire on whether or not alcohol-containing alcohol is consumed And collecting information about alcohol (or drinking) by inducing user input.

In another example, the external device may collect information about alcohol by receiving input from the user as to whether or not the alcohol-containing food such as alcohol has been consumed.

In yet another embodiment, the external device may receive information about alcohol (or whether it is drinking) or the like from an external device linked breath measuring device (or an alcohol measurement sensor in an external device).

In the case of blood pressure, an external device can receive information on blood pressure from other external devices when measuring blood pressure in other external devices such as a blood pressure monitor. For example, an external device may receive information about blood pressure from a personal blood pressure monitor or a publicly available blood pressure monitor device (e.g., a blood pressure monitor device provided at an institution / hospital). In another example, the external device may download information about blood pressure from a PHR (personal health record) server associated with the hospital's medical record. In one embodiment, when the external device receives information on blood pressure from another external device, server, or the like, the external device may calculate information on the received blood pressure and calculation The accuracy of the estimated value of the blood pressure can be improved by comparing the estimated blood pressure values and adjusting the coefficients such as the scale factors.

In the case of stress, in one embodiment, the external device can determine the stress level by measuring / analyzing heart rate variability using a heart rate sensor or the like.

In the case of a sleep state, for example, the sleep state can be tracked (or measured) in an external device (e.g., a wearable device). In another example, the external device may receive information about the sleep state measured using a pressure sensor, RF sensor, etc., of another external device (e.g., a bed). In one embodiment, the sleep is awakened from the parasympathetic nerves activated during sleep when awakened from the water, and the sympathetic nerves are exaggerated so that the relationship between the two autonomic nerves can be reversed. Thus, arrhythmias can occur frequently in the early morning when the function of the sympathetic nerves is most abruptly activated.

In the case of an external temperature, it may be measured at an external device (e.g., a portable terminal, etc.) or measured from a server and received at an external device. In one embodiment, cold weather increases the risk of arrhythmia, contracts the blood vessels and may cause clogging of blood vessels, such as thrombosis, or elevation of blood pressure.

In Figure 1603, the controller 140 can determine an arrhythmogenic factor based on the acquired biometric information and external environment information. For example, the control unit 140 can obtain information on the amount of caffeine intake (or intake), the alcohol concentration (or drinking status), the amount of carbon monoxide, the stress level, the user's sleep state, the external temperature, have. The control unit 140 may determine a specific factor measured (or detected) before or after a certain time point of the arrhythmia occurrence as the arrhythmogenic factor among the confirmed information. For example, when the caffeine intake amount is measured above the reference value, the control unit 140 may determine caffeine as an arrhythmogenesis factor. In another example, the control unit 140 may determine alcohol (or alcohol) as an arrhythmogenic agent when the measured alcohol concentration is measured above a reference value.

In one embodiment, the process of determining an arrhythmogenic factor may be performed in an external device. For example, the external device may receive the biometric information and the like obtained from the arrhythmia detecting device 100 from the arrhythmia detecting device 100. For example, the external device receives biometric information such as an electrocardiogram signal, heart ballistic signal, and / or cardiac sound signal, information on the detected arrhythmia existence or arrhythmia type, and external environment information from the arrhythmia detection device 100 . The external device can determine the arrhythmogenic factor based on the received biometric information or the like.

In one embodiment, an external device may determine (or estimate) an arrhythmogenic factor. When the external device determines the arrhythmogenesis factor, the external device may transmit information on the triggering factor determined by the arrhythmia detection device 100. For example, an external device may be configured to detect an arrhythmia when the user is currently taking action (e.g., during caffeine ingestion or intense exercise) or the user's current living condition is above a threshold (e.g., high stress figure, (Eg, awake from the surface of the water, etc.), or that the external environmental condition is above the threshold (eg, low temperature, etc.). In another embodiment, the external device may be configured to detect an arrhythmia when the user's actions accumulated over a period of time (e.g., a day or after awakening) are above a threshold (e.g., caffeine ingested during a day, (Eg, the number of times of stress during the day, the number of times of hypertension during the day, etc.) can be determined as the arrhythmogenesis factor.

In one embodiment, the controller 140 may determine an arrhythmogenesis factor as a factor determined to be highly correlated with arrhythmia detection over a period of time (e.g., a month). For example, when the arrhythmia is detected for a certain period of time, when the number of times that the stress level is abnormally measured is large, the controller 140 may determine the stress as an arrhythmogenesis factor.

In step 1605, the control unit 140 may store the arrhythmogenic factor in the storage unit 130. For example, the control unit 140 may store the arrhythmogenic factor in the storage unit 130 for each user.

In one embodiment, the control unit 140 may transmit the arrhythmogenic factor to an external device. When an external device receives an arrhythmogenic agent, the external device may store the arrhythmogenic agent.

In another embodiment, when an arrhythmogenic factor is determined in an external device, the external device may store the determined arrhythmogenic factor. The external device may transmit information on the determined arrhythmogenic factor to the arrhythmia detection device 100 or a server or the like.

In another embodiment, when an arrhythmogenic factor is determined in a server or the like, a server or the like may store the determined arrhythmogenic factor. The server or the like can transmit information on the determined arrhythmogenic factor to the arrhythmia detection device 100 or an external device or the like.

17 is a flowchart illustrating a method of informing the risk of an arrhythmia and the reduction of an arrhythmia according to an embodiment of the present invention. The method described in Fig. 17 can be executed in the arrhythmia detecting apparatus 100 or an external electronic device (for example, a wearable device). However, for convenience of explanation, it is assumed that it is executed in an external electronic device.

In step 1701, the external electronic device can acquire biometric information or external environment information. For example, the external electronic device can measure the biological information (e.g., the amount of caffeine measured in the body, the concentration of alcohol, the amount of carbon monoxide, the stress level, the user's sleep state, Can be obtained. In another example, the external electronic device can acquire motion information of the user through the acceleration sensor 127, external temperature information through the temperature sensor, and external humidity information through the humidity sensor.

In step 1703, based on the acquired biometric information or external environment information, the external electronic device can check whether the arrhythmogenic factor stored in the storage unit 130 is abnormally measured. For example, the external electronic device can confirm whether the acquired biometric information or external environment information is an arrhythmogenic factor stored in the storage unit 130. In one embodiment, the arrhythmogenic factor may correspond to a factor that is highly correlated with arrhythmogenesis to the user, as described in FIG. When the external electronic device confirms that the acquired biometric information or external environment information corresponds to the arrhythmogenic factor, the external electronic device can confirm whether the biometric information or the external environment information is abnormally measured. For example, if an external electronic device confirms that caffeine corresponds to an arrhythmogenic agent, the external electronics can verify that the amount of caffeine is measured above the reference value.

In step 1705, the external electronic device may output an arrhythmia risk alert. For example, an external electronic device can output a high probability of arrhythmia as the amount of caffeine correlated with arrhythmia is measured.

In one embodiment, the external electronic device may output light, voice, or vibration to signal the risk of arrhythmia.

In step 1707, the external electronic device may output an arrhythmia reduction method notification. For example, an external electronic device may output a warning signal to reduce caffeine intake to reduce arrhythmias if the arrhythmogenic agent is caffeine.

In one embodiment, in one embodiment, the external electronics may output light, voice, or vibration to signal an arrhythmia reduction method.

FIG. 18 is a diagram illustrating an arrhythmia risk and an arrhythmia reduction notification according to an embodiment of the present invention. FIG.

Referring to FIG. 18, the external electronic device 1801 can output a warning message indicating the risk of arrhythmia such as "when caffeine is ingested, the risk of arrhythmia is high" when the arrhythmogenic agent is caffeine. The external electronic device 1801 may output a notification 1803 such as "Please eat less caffeine" to lower the risk of arrhythmia. In another example, the external electronic device 1801 consumed two cups of coffee today. OOO is highly likely to develop arrhythmia when taking caffeine, so we recommend that you take two cups of coffee per day. "

FIG. 19 is a flowchart for explaining a method of outputting a reattachment notification when there is an error in the attachment of the arrhythmia detecting device 100, according to an embodiment of the present invention.

Referring to FIG. 19, in step 1901, the controller 140 may acquire an electrocardiogram signal from the electrocardiogram sensor 121.

In step 1903, the control unit 140 can check whether there is an error in the attachment of the arrhythmia detecting device 100. [ In one embodiment, the arrhythmia detection apparatus 100 can check whether the attachment of the arrhythmia detection apparatus 100 is in error by measuring the waveform of the obtained electrocardiogram signal and analyzing the measured waveform. In another embodiment, the control unit 140 may detect the attachment angle of the arrhythmia detection device 100 based on the obtained acceleration signal. If the detected attachment angle is out of the preset range, It is possible to judge that there is an error in the attachment state of the lens. In another embodiment, the control unit 140 may obtain an electrocardiogram signal and analyze the signal-to-noise ratio. The controller 140 can determine that there is an error in the patch attachment state as in the case where some of the electrodes are not in close contact when the signal-to-noise ratio is continuously lower than a predetermined threshold value (i.e., when noise is large) for a predetermined time. If there is an error in the attachment state of the arrhythmia detecting device 100, the control unit 140 may output a notification so that the patch attachment state can be confirmed, or may transmit the notification to the external device through the communication unit. In another embodiment, the control unit 140 may output a notification to guide the arrhythmia detecting apparatus 100 to attach the arrhythmia detecting apparatus 100 at an angle at which the correct waveform appears, or may transmit an alert to the external apparatus through the communication unit. For a detailed description, reference is made to Fig. 20 below.

FIG. 20 is an exemplary diagram of an electrocardiogram signal measured when there is an error in the attachment of the arrhythmia detecting device 100 according to an embodiment of the present invention.

Referring to FIG. 20, in one embodiment, 2001 may be a diagram corresponding to a case where the arrhythmia detecting apparatus 100 is normally attached to a user without error. For example, as shown in 2001, when the arrhythmia detection apparatus 100 is attached to the user's body in a direction parallel to the gravitational acceleration direction, the electrocardiogram signal can be normally measured. For example, the control unit 140 may set a direction parallel to the gravitational acceleration direction as a reference direction. When the arrhythmia detecting apparatus 100 is attached to the body in the reference direction, distortion-free electrocardiogram signal waveforms can be measured.

2003 shows an arrhythmia detecting apparatus 100 that is rotated to the left by a roll angle? From the direction of the arrhythmia detecting apparatus 100 of 2001, and the measured electrocardiogram signal. In one embodiment, the roll angle at which the arrhythmia detection apparatus 100 is rotated can be measured through the acceleration sensor 127. [

2005 illustrates an arrhythmia detecting apparatus 100 that has been rotated to the right by a roll angle? From the direction of the arrhythmia detecting apparatus 100 of 2001, and the measured electrocardiogram signal.

Comparing 2001 to 2005, it can be seen that the electrocardiogram signal waveforms in 2003 and 2005 are distorted compared to the 2001 electrocardiogram signal waveforms. For example, when comparing 2001 and 2003, the size of the R peak point in 2003 is smaller than that of 2001, and in the case of T wave, the sign can be inverted. In another example, when comparing 2001 and 2005, the size of the Q point in 2005 can be measured smaller than in 2001.

In one embodiment, the controller 140 designates the electrocardiogram signal waveform without distortion as a reference electrocardiogram signal, and confirms the attachment error of the arrhythmia detection device 100 through comparison with the obtained electrocardiogram signal. For example, the control unit 140 can designate the electrocardiogram signal waveform shown in 2001 as the reference electrocardiogram signal waveform. The control unit 140 can check the attachment error of the arrhythmia detection apparatus 100 by comparing the electrocardiogram signal waveform obtained in 2003 and the signal waveform between the reference electrocardiogram signal waveform.

The control unit 140 controls the display unit 140 to output a notification for the reattachment of the arrhythmia detection device 100 in step 1905. When the control unit 140 confirms the attachment error of the arrhythmia detection device 100, can do.

In one embodiment, the controller 140 detects an angle (e.g., roll angle?) And may use it for ECG processing. For example, the controller 140 may store information on the electrocardiographic waveform pattern at the corresponding angle. For example, the control unit 140 may store information on a state in which the right side is rotated rightward by a roll angle? From the direction of the arrhythmia detection device 100 and a pattern in which an R peak is small, as in 2005. The controller 140 can adjust the peak detection threshold value to a low value when using the R-peak detection algorithm. In another example, when the controller 140 detects that the electrodes are inverted and attached through the acceleration signal, the controller 140 can change the signal channel for each electrode to process the signal. This prevents the electrocardiogram waveform from being inverted and distorted.

FIG. 21 is an exemplary diagram for explaining a method of outputting a reattachment notification when there is an error in the attachment of the arrhythmia detecting device 100, according to an embodiment of the present invention.

Referring to FIG. 21, when there is an error in the attachment of the arrhythmia detection device 100 in 2101, the arrhythmia detection device 100 can transmit information on the attachment error to the external electronic device 2111. In one embodiment, the external electronic device may output a reattachment notification such as "reattach!" When information on an attachment error of the arrhythmia detection device 100 is received. In another embodiment, the external electronic device can output a notification such as "please turn the device 3 degrees" when information on the attachment error of the arrhythmia detection device 100 is received.

When the attachment of the arrhythmia detecting device 100 is correctly attached without error in 2103, the arrhythmia detecting device 100 can transmit information on the normal attachment to the external electronic device. In one embodiment, the external electronic device may output a notification informing that the arrhythmia detection apparatus 100 is normally attached, such as "during normal measurement!" When information on the normal attachment of the arrhythmia detection apparatus 100 is received .

As described above, the method for detecting an arrhythmia according to various embodiments of the present invention and the arrhythmia occurring to a user supporting the arrhythmia can be accurately measured.

The above-described embodiments of the present invention can be embodied in a general-purpose digital computer that can be embodied as a program that can be executed by a computer and operates the program using a computer-readable recording medium. In addition, the structure of the data used in the above-described embodiments of the present invention can be recorded on a computer-readable recording medium through various means. The computer-readable recording medium includes a storage medium such as a magnetic storage medium (e.g., ROM, floppy disk, hard disk, etc.), optical reading medium (e.g., CD-ROM, DVD, etc.)

The present invention has been described with reference to the preferred embodiments. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.

100: arrhythmia detection device
110:
120:
130:
140:

Claims (24)

A method for detecting an arrhythmia,
Acquiring an electrocardiogram signal;
Calculating a signal-to-noise ratio of the obtained electrocardiogram signal; And
And determining whether to use another bio-signal according to the calculated signal-to-noise ratio. The method according to claim 1,
Wherein the other vital signal comprises at least one of a cardiac tidal signal and a cardiac signal. 3. The method of claim 2,
Determining whether to use the other bio-signal according to the calculated signal-to-noise ratio, comprising: comparing the calculated signal-to-noise ratio and a designated first threshold value;
Detecting a feature point of the electrocardiogram signal using the another bio-signal when the calculated signal-to-noise ratio is less than or equal to the designated first threshold value; And
And determining whether or not an arrhythmia occurs based on the feature points of the detected electrocardiogram signal. The method of claim 3,
Wherein the step of detecting the feature point of the electrocardiogram signal using the another bio-
Detecting a feature point of the other bio-signal; And
Detecting an R peak point of the electrocardiogram signal using the detected characteristic point of the other bio-signal,
Wherein the step of detecting an R peak point of the electrocardiogram signal comprises:
Specifying a predetermined time range from a detected characteristic point of the another bio-electrical signal; And
And detecting the R peak point using the electrocardiogram signal within the predetermined time range from the detected characteristic point of the other bio-signal. 5. The method of claim 4,
The detected feature point of the other bio-
A J peak point of the heart ballistic signal, and a peak point corresponding to a ventricular systole of the cardiac signal. 3. The method of claim 2,
Determining whether to use the other bio-signal according to the calculated signal-to-noise ratio,
Comparing the calculated signal-to-noise ratio and a second threshold value; And
Determining whether an arrhythmia is generated based on the other bio-signal instead of the electrocardiogram signal when the calculated signal-to-noise ratio is less than or equal to the designated second threshold value. The method according to claim 1,
The other bio-signal is a cardiac trait signal,
Determining an arrhythmia occurrence based on at least one of the electrocardiogram signal and the cardiac trajectory signal;
Calculating a blood pressure based on the electrocardiogram signal and the cardiac trajectory signal;
Determining whether the arrhythmia has occurred and the risk of an arrhythmia based on the calculated blood pressure, and transmitting notification information to an external device according to the determined risk of an arrhythmia. The method according to claim 1,
Determining an item out of a preset range of the user's biometric information or external environment information as an argument that causes arrhythmia when the arrhythmia is detected; And
And storing a factor inducing the determined arrhythmia. 9. The method of claim 8,
Wherein said item comprises at least one of blood pressure, stress, temperature, caffeine intake, alcohol consumption, smoking amount, sleep state. The method according to claim 1,
Determining a frequency or accumulation amount of an item that deviates from a preset range of a user's biometric information or external environment information during a certain period of time to a threshold value to determine an arrhythmia-inducing factor when the arrhythmia is detected; And
And storing a factor inducing the determined arrhythmia. 11. The method of claim 10,
Measuring a current user's biometric information or external environment information; And
If at least a part of the items included in the measured biometric information or external environment information of the current user corresponds to a factor inducing the arrhythmia and is out of a predetermined range, transmitting alert information on the risk of arrhythmia to an external device ≪ / RTI > In the arrhythmia detection device,
A sensor unit for acquiring an electrocardiogram signal; And
And a control unit,
The control unit,
Calculating a signal-to-noise ratio of the obtained electrocardiogram signal,
And determines whether to use the other biological signal according to the calculated signal-to-noise ratio. 13. The method of claim 12,
Wherein the other biological signal is at least one of a heart valve signal and a heart sound signal. 14. The method of claim 13,
The control unit,
Comparing the calculated signal-to-noise ratio and a designated first threshold value, and detecting a feature point of the electrocardiogram signal using the other bio-signal when the calculated signal-to-noise ratio is less than or equal to the designated first threshold value, And determining whether an arrhythmia is generated based on the feature points of the detected electrocardiogram signal. 15. The method of claim 14,
The control unit,
Detecting a feature point of the other bio-signal to detect a feature point of the electrocardiogram signal using the another bio-signal, detecting an R-peak point of the electrocardiogram signal using the detected feature point of the other bio- In addition,
Wherein a range of time from a detected characteristic point of the other bio-signal is specified to detect an R peak point of the electrocardiogram signal and a voltage of the electrocardiogram signal within the predetermined time range from a detected characteristic point of the other bio- And determines a peak point as the R peak point. 16. The method of claim 15,
Wherein the detected feature point of the other bio-signal comprises at least one of a J peak point of the cardiac trajectory signal and a peak point corresponding to a ventricular systole of the cardiac signal. 14. The method of claim 13,
The control unit,
And comparing the calculated signal-to-noise ratio and a designated second threshold value. When the calculated signal-to-noise ratio is equal to or less than the second threshold value, whether or not an arrhythmia is generated based on the other bio- Determining an arrhythmia detection device. 13. The method of claim 12,
The other bio-signal is a cardiac trait signal,
The control unit,
Determining an occurrence of an arrhythmia based on at least one of the electrocardiogram signal and the cardiac tropon signal, calculating a blood pressure based on the electrocardiogram signal and the cardiac trajectory signal, and determining whether the arrhythmia has occurred or not based on the calculated blood pressure, To determine the risk of an arrhythmia, and to transmit notification information through a communication unit. 13. The method of claim 12,
The control unit,
An arrhythmia detection device for determining an item that deviates from a preset range of a user's biometric information or external environment information when the arrhythmia is detected as a factor that induces an arrhythmia and stores the factor that causes the determined arrhythmia. 20. The method of claim 19,
Wherein the item includes at least one of blood pressure, stress, temperature, caffeine intake amount, drinking amount, smoking amount, and sleeping state. 13. The method of claim 12,
The control unit,
Determining a factor that induces an arrhythmia by comparing the occurrence frequency or accumulation amount of an item that deviates from a preset range of the user's biometric information or external environment information during a certain period of time to a threshold value when the arrhythmia is detected, / RTI > 22. The method of claim 21,
The control unit,
The method comprising the steps of: measuring current user's biometric information or external environment information; and when at least a part of the items included in the measured biometric information or external environment information of the current user corresponds to a factor causing the arrhythmia, And transmits notification information on the risk of occurrence to an external device. 13. The method of claim 12,
Further comprising an acceleration sensor for sensing an angle at which the arrhythmia detection device is attached,
The control unit,
An arrhythmia detection device that detects an attachment error of the arrhythmia detection device based on the information about the sensed angle from the acceleration sensor and transmits a reattachment notification of the arrhythmia detection device to an external device when the attachment error is detected, . A method for detecting an arrhythmia,
Acquiring an electrocardiogram signal;
Obtaining at least one of a heart ballistic signal or a cardiac sound signal; And
And detecting the arrhythmia using the obtained ECG signal, the cardiovascular signal and / or the cardiac signal, or the heart valve signal and / or the heart sound signal.

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