JP6709116B2 - Respiration detection device, respiration detection method, and respiration detection program - Google Patents

Description

The present invention relates to a respiratory detection device, a respiratory detection method, and a respiratory detection program, and more particularly, to a respiratory that can detect the respiratory rate of the subject based on an electrocardiogram detected by the subject. The present invention relates to a detection device, a respiratory detection method, and a respiratory detection program.

2. Description of the Related Art A breathing detection device for detecting a breathing state of a subject (patient or the like) is conventionally known. As a general respiratory detection device, for example, a device called a flow sensor, a thermistor sensor, or a respiratory band sensor is used (see, for example, Patent Document 1 (paragraph [0002] etc.)).

The flow sensor is a device that monitors a change in air flow (air flow state) associated with breathing by a sensor attached to the nose or mouth. The breathing state of the subject is detected based on the detected airflow state. Flow sensors are often used to examine apnea and hypopnea during sleep.

The thermistor sensor is a device that detects a temperature that changes with breathing by a sensor for temperature detection attached to the nose or mouth. Since the resistance value of the thermistor changes with the temperature change, the breathing state of the subject can be detected based on the change in the resistance value. In general, a breath exhaled and a breath exhaled by a person have different breath temperatures. Therefore, the expiratory and inhaled breaths can be measured by utilizing the difference in breath temperature.

The breathing band sensor is a device that detects the breathing state of the subject by mounting a belt with a strain gauge (strain gauge) on the chest. Generally, when an external force is applied to expand or contract a metal (resistor), the resistance value of the metal increases or decreases with the expansion or contraction. For this reason, a resistor is attached to the chest of the subject, and the change state of the resistance value is detected. For example, when the subject breathes, the chest is expanded and contracted as the subject breathes. When the chest is expanded or contracted, the resistance element attached to the chest expands or contracts, so that the resistance value between the electrodes changes. The breathing state of the subject is detected based on the changing resistance value.

However, in the case of the flow sensor or the thermistor sensor, it is necessary to attach a physical sensor to the nose or the tip of the mouth, so that the subject feels uncomfortable when detecting the breathing state. Further, since it is necessary to physically attach the sensor, there is a problem in that the burden of attachment or the like is caused and the convenience of the device is poor. In addition, it has been difficult to attach a sensor to a subject and continuously measure breathing in daily life. Furthermore, since it is necessary to disinfect the sensor after using the sensor (after measuring the breathing), a burden is imposed on the maintenance of the device.

Further, in the case of the respiratory band sensor, the chest is in a state of being fastened with the belt in order to detect the movement of the chest. For this reason, there is a problem that the subject feels a sense of discomfort and a sense of restraint when the breathing band sensor is used. Moreover, there is a problem that it is difficult to continuously measure in daily life because the chest is tightened.

In consideration of such a problem of the respiratory detection device, there has been proposed a method of detecting a respiratory state by using data detected by different detection devices. For example, a method of detecting the respiratory state of the subject based on the waveform state of the electrocardiogram data of the subject has been proposed. Here, the electrocardiogram data is data indicating the activity of the heart as an electric signal (electrical waveform).

A general waveform detected by electrocardiogram data can be schematically shown by a P wave, a QRS wave, and a T wave, as shown in FIG. In the waveform of the electrocardiogram data, a small dome-shaped P wave is generated first, then a tall upward QRS wave is generated, and finally, a slightly large dome-shaped T wave is generated. The QRS wave has a waveform formed by the apex R point and the Q and S points shown downwards before and after the apex. Generally, the QRS wave is judged as an integral waveform composed of Q point, R point and S point.

In the electrocardiogram data, a PQRST wave that is a combination of P wave, QRS wave, and T wave is repeatedly generated for each heartbeat. The PQRST wave for one heartbeat shows a waveform corresponding to one heartbeat. Of the PQRST waves, the P wave indicates the excitation (contraction) of the atrium, the QRS wave indicates the excitation (contraction) of the ventricle, and the T wave indicates the state (expansion) of the ventricle entering the excitable state.

The electrocardiogram data is obtained by detecting the excitement state of the atrium or ventricle of the heart as an electric signal as described above, but it is known that the electrocardiogram data may include an electric signal associated with a respiratory action ( See, for example, Non-Patent Document 1). Generally, not only for forced breathing in which the respiratory muscles are mobilized for breathing, but also for resting breathing, the muscles move due to the breathing action, so the movements of the muscles are detected as electrical signals. For example, an electrical signal is generated by the activity of the sternocleidomastoid muscle, the internal intercostal muscle, and the auxiliary respiratory muscles such as the abdominal muscle associated with forced breathing. An electric signal is also generated by the movement of muscles such as the chest and shoulders associated with breathing. The electric signals relating to the movement of these muscles are included in the electrocardiogram data as electromyographic data.

Therefore, if only the electrical signal associated with the breathing motion can be extracted from the electrocardiogram data, the respiratory state of the subject can be determined from the electrocardiogram data. In particular, since it is possible to continuously detect electrocardiogram data in daily life by using a Holter electrocardiograph or the like which is generally commercially available, it becomes possible to continuously detect the respiratory state in daily life. .. However, since the electrical signal associated with the respiratory action is a weaker signal than the electrical signal detected with the heartbeat, it is difficult to extract only the electrical signal associated with the respiratory action from the electrocardiogram data.

In order to solve such a problem, in recent years, a method of detecting a respiratory state by removing an electrical signal indicating an excitable state of the heart, that is, a component of a PQRST wave from electrocardiogram data has been studied (for example, non-patent reference). Reference 2 and non-patent reference 3).

As a method of detecting a respiratory state from electrocardiogram data, for example, by using orthogonal dipole leads of X, Y, Z (orthogonal three-axis directions), each electrocardiogram data is derived, and then the signal obtained by each lead is analyzed. The low frequency cutoff at 120 Hz reduces the low frequency components of the PQRST wave. After that, a method has been proposed in which a respiratory waveform is extracted by time-decay integration of a three-dimensional vector magnitude value of a signal obtained by X, Y, Z induction.

Patent No. 5323532

Hiroshi Sekiguchi, Yutaka Kondo, Ichiro Kukita, “Evaluation of forced respiration by activity analysis of assisting respiratory muscles using surface electromyography”, artificial respiration, Japanese Society of Respiratory Therapy, published April 1, 2013, Vol. 30 No. 1 Masatoshi Hara, 7 others, “A proposal for respiratory detection method during LP measurement”, 25th Body Surface Micropotential Study Group Program/Abstract, Body Surface Micropotential Study Group, February 28, 2015 Masatoshi Hara, 7 others, "Respiratory EMG Detection Method Using Holter Electrocardiography-How to Eliminate ECG-", 31st ECG Information Processing Workshop Abstracts, October 21, 2015, p. ． 18

However, the method of blocking the electrocardiogram data in the low frequency range at 120 Hz tends to reduce the signal component associated with the breathing motion by the reduction blocking process. Therefore, there is a problem that it is difficult to obtain the respiratory waveform with sufficient detection accuracy. Further, although the low frequency cutoff of 120 Hz can reduce the waveform component of the PQRST wave, the PQRST wave component is also included in the frequency components of 120 Hz and above, so that part of the PQRST wave component remains. Therefore, the method of blocking the electrocardiogram data in the low frequency range at 120 Hz has a problem that the signal level (amplitude) of the PQRST wave remains as noise, and it is difficult to sufficiently detect the signal component of the respiratory state. Further, since the electromyogram related to respiration also includes a component of 120 Hz or less, if it is cut off at 120 Hz, there is a problem that the electromyographic component becomes small and the extracted respiratory waveform becomes weak.

The present invention has been made in view of the above problems, and a respiratory detection device capable of detecting a respiratory state from electrocardiographic data of a subject by removing a PQRST wave component from electrocardiographic data, and respiratory detection. It is an object to provide a method and a respiratory detection program.

In order to solve the above-mentioned problems, a respiratory detection device according to the present invention is a basic signal extraction means for continuously extracting a basic signal containing a PQRST wave for one heartbeat from electrocardiogram data of a subject, and the basic signal. The recording means for recording the basic signal of the subject extracted in the past by the extracting means as a template signal, and the template signal recorded in the recording means from the basic signal extracted by the basic signal extracting means, The difference between the residual signal generation means for generating a residual signal by removing the component of the PQRST wave from the electrocardiogram data by subtraction and the amplitude value of the residual signal at constant time intervals is obtained as a difference, and the change in the difference is calculated. And a respiratory rate detecting means for detecting a respiratory rate per unit time based on a change in the waveform of the respiratory wave. And means.

Further, the breathing detection method according to the present invention includes a basic signal extraction step of continuously extracting a basic signal including a PQRST wave for one heartbeat from the electrocardiogram data of the subject, and a past extraction in the basic signal extraction step. The basic signal of the subject is recorded in the recording means as a template signal, the template signal read from the recording means, by subtracting from the basic signal extracted in the basic signal extraction step, A residual signal generation step of generating a residual signal from which the PQRST wave component has been removed from the electrocardiogram data, and a difference between amplitude values of the residual signal at constant time intervals are obtained as a difference, and changes in the difference are cumulatively obtained. Accordingly, a respiratory wave calculation step of calculating a respiratory wave indicating the respiratory state of the subject, and a respiratory rate detection step of detecting a respiratory rate per unit time based on the waveform change of the respiratory wave are provided. Is characterized by.

Furthermore, the respiration detection program according to the present invention is a respiration detection program for a respiration detection device that detects the respiration rate of the subject based on the electrocardiogram data of the subject, and the respiration detection device records. And a basic signal extraction function for causing the control means of the respiration detection device to continuously extract a basic signal containing one PQRST wave of one heartbeat from the electrocardiogram data, and the basic signal extraction function extracts the signal in the past. The extracted basic signal of the subject is recorded in the recording means as a template signal, and the template signal read from the recording means is subtracted from the basic signal extracted by the basic signal extracting function. As a result, a residual signal generation function for generating a residual signal by removing the PQRST wave component from the electrocardiogram data and a difference between the amplitude values of the residual signal at constant time intervals are obtained as a difference, and the change in the difference is accumulated. And a respiratory rate detection function for detecting a respiratory rate per unit time based on a change in the waveform of the respiratory wave. It is characterized by realizing.

In the respiration detection device, the respiration detection method, and the respiration detection program according to the present invention, the template signal is subtracted from the basic signal including the PQRST wave for one heartbeat detected by the subject, so that the PQRST wave is extracted from the electrocardiogram data. The component of is removed. Here, the template signal is electrocardiogram data (basic signal) extracted in the past from the same subject.

The PQRST wave of general electrocardiogram data tends to have different waveform characteristics depending on the subject. Therefore, like the respiratory detection device according to the present invention, by using the basic signal extracted in the past by the same subject as the template signal, the waveform of the template signal and the PQRST wave extracted from the electrocardiogram data The waveform and are similar. Therefore, by subtracting the PQRST wave of the electrocardiogram data detected with the pulsation of the subject's heart with the PQRST wave of the template signal generated by the same subject, the PQRST wave is effectively generated from the electrocardiogram data. It becomes possible to remove the component of.

Further, the residual signal obtained by removing the PQRST wave component from the electrocardiogram data includes an electric signal indicating the movement of the muscles associated with the breathing motion of the subject. However, the amplitude change (voltage change) of the respiratory motion included in the amplitude change of the residual signal is smaller than that of the PQRST wave component showing the amplitude change associated with the heartbeat. Therefore, if the residual signal is left as it is, it affects the accuracy of grasping the respiratory state that changes with the movement of the muscle.

Therefore, in the respiratory detection device, the respiratory detection method, and the respiratory detection program according to the present invention, the contour line of the residual signal is calculated as the respiratory wave. That is, the difference between the amplitude values in which the residual signal changes at regular time intervals is obtained as a difference, and the change in the obtained difference is cumulatively obtained to calculate the respiratory wave indicating the respiratory state of the subject. In this way, by obtaining the difference in amplitude change (difference in amplitude value) of the movement of the muscles associated with the breathing action and cumulatively obtaining the change in the difference, the breathing action having less amplitude change than the PQRST wave component is obtained. It becomes possible to reveal the movement of muscles. The respiratory rate of the subject can be detected from the electrocardiogram data by obtaining the waveform change per unit time based on the respiratory wave in which the movement of the muscle during the breathing motion is revealed.

Further, in the above-described breathing detection apparatus, at least one or more template signals are recorded in the recording means, and the waveform of the basic signal extracted by the basic signal extracting means and the recording means record the waveform. A template for extracting only one template signal having the highest correlation coefficient by obtaining the correlation coefficient with the waveform of the generated template signal for all the template signals recorded in the recording means. The residual signal generation means comprises signal extraction means, and the residual signal generation means subtracts one template signal extracted by the template signal extraction means from the basic signal extracted by the basic signal extraction means to obtain the residual signal. It may generate a signal.

Further, in the above-mentioned breath detection method, at least one or more of the template signals are recorded in the recording means, and the waveform of the basic signal extracted in the basic signal extraction step and the reading by the recording means are performed. By obtaining the correlation coefficient with the waveform of the outputted template signal for all the template signals recorded in the recording means, only one template signal having the highest correlation coefficient is extracted. A template signal extracting step, wherein in the residual signal generating step, the template signal extracted only in the template signal extracting step is subtracted from the basic signal extracted in the basic signal extracting step, It may be one that generates a residual signal.

Further, in the above-mentioned respiration detection program, at least one or more of the template signals are recorded in the recording means, and the control means stores the waveform of the basic signal extracted by the basic signal extracting function. , The template signal showing the highest correlation coefficient by obtaining the correlation coefficient with the waveform of the template signal read from the recording means for all the template signals recorded in the recording means. Of the template signal extracted by the template signal extraction function from the basic signal extracted by the basic signal extraction function in the residual signal generation function. The residual signal may be generated by subtracting.

In the respiration detection device, the respiration detection method, and the respiration detection program according to the present invention, the correlation coefficient between the waveform of the basic signal and the waveform of the template signal is obtained. Further, the process of obtaining the correlation coefficient is performed for all the template signals recorded in the recording means. For example, when n template signals are recorded in the recording means, n template signals are calculated in order to obtain the correlation coefficient between each of the template signals and the basic signal extracted from the electrocardiogram data of the subject. The correlation coefficient of is calculated.

The waveform of the template signal showing the highest correlation coefficient among the obtained n correlation coefficients is the waveform most similar to the waveform of the basic signal extracted from the electrocardiogram data of the subject. Can be judged. Therefore, by subtracting the template signal having the highest correlation coefficient from the basic signal extracted from the electrocardiogram data of the subject, the PQRST wave component can be effectively removed from the electrocardiogram data. It will be possible.

Further, in the above-described respiration detection device, the template signal extraction means, while gradually shifting the time position of the R point in the PQRST wave of the template signal with reference to the R point of the PQRST wave included in the basic signal, The residual signal generating means calculates the correlation coefficient, and the residual signal generating means uses the template signal extracting means to extract 1 from the basic signal extracted by the basic signal extracting means at the time position where the correlation coefficient shows the highest value. The residual signal may be generated by subtracting only one of the extracted template signals.

Further, in the above-described respiratory detection method, in the template signal extraction step, the time position of the R point in the PQRST wave of the template signal is gradually shifted with reference to the R point of the PQRST wave included in the basic signal. In the residual signal generation step, the correlation signal is obtained in the template signal extraction step from the basic signal extracted in the basic signal extraction step at the time position where the correlation coefficient shows the highest value. The residual signal may be generated by subtracting only one template signal extracted.

Further, in the above-described respiration detection program, the template signal extraction function is configured to control the control unit to determine the R point of the PQRST wave of the template signal with reference to the R point of the PQRST wave included in the basic signal. The basic signal extracted by the basic signal extraction function at the time position at which the correlation coefficient shows the highest value in the residual signal generation function, while causing the correlation coefficient to be obtained while shifting the time position little by little. From the above, the residual signal may be generated by subtracting only one template signal extracted by the template signal extraction function.

In the respiration detection device, the respiration detection method, and the respiration detection program according to the present invention, the R point of the PQRST wave included in the basic signal is used as a reference, and the time position of the R point in the PQRST wave of the template signal is gradually shifted while By obtaining the relation number, it is possible to obtain the correlation coefficient at the time position where the PQRST wave components of the signals are most similar to each other.

Therefore, at the time position where the correlation coefficient shows the highest value, the electrocardiogram is obtained by subtracting the template signal showing the highest correlation coefficient from the basic signal extracted from the electrocardiogram data of the subject. It becomes possible to more effectively remove the PQRST wave component from the data.

In addition, the above-described respiration detection device synthesizes the basic signal extracted by the basic signal extraction means and the template signal extracted by the template signal extraction means to obtain only one new template signal. A template signal generating unit for generating the template signal, and a template signal updating unit for recording the new template signal generated by the template signal generating unit in the recording unit instead of the template signal extracted by the one The residual signal generation means generates the residual signal by subtracting the new template signal generated by the template signal generation means from the basic signal extracted by the basic signal extraction means. It may be.

Further, in the above-described breathing detection method, a new template signal is generated by combining the basic signal extracted in the basic signal extraction step and the template signal extracted only one in the template signal extraction step. A template signal generation step of generating the template signal; and a template signal updating step of recording the new template signal generated in the template signal generation step in the recording means in place of the template signal extracted by the one In the residual signal generating step, the residual signal is generated by subtracting the new template signal generated in the template signal generating step from the basic signal extracted in the basic signal extracting step. It may be.

Further, in the above-described respiration detection program, the control means may combine the basic signal extracted by the basic signal extraction function and the template signal extracted only one by the template signal extraction function. A template signal generating function for generating a new template signal, and a template for recording in the recording means in place of the template signal extracted by the one template signal generated by the template signal generating function A signal updating function and, in the residual signal generating function, subtracting the new template signal generated by the template signal generating function from the basic signal extracted by the basic signal extracting function, It may generate a residual signal.

In the respiratory detection device, respiratory detection method, and respiratory detection program according to the present invention, by combining the basic signal extracted from the electrocardiogram data of the subject and the template signal showing the highest correlation coefficient, , Generate a new template signal. By generating a new template signal by the synthesis process, the waveform of the template signal before synthesis can be improved to a waveform similar to the waveform of the basic signal extracted from the electrocardiogram data. Therefore, by subtracting the synthesized new template signal from the basic signal extracted from the electrocardiogram data of the subject, the subtraction process can be performed using a waveform having higher similarity. Therefore, the component of the PQRST wave can be more effectively removed from the electrocardiogram data.

Further, in the above-mentioned breath detection device, when the correlation coefficient showing the highest value is equal to or less than a preset threshold value, the basic signal extracted by the basic signal extracting means is changed to a new template signal. As a residual signal generating means, the residual signal generating means additionally records a template signal to the recording means, when the correlation coefficient showing the highest value is equal to or less than a preset threshold value, the basic signal extracting means. The residual signal may be generated by subtracting the new template signal additionally recorded by the template signal adding means from the basic signal extracted by.

Further, in the above-described respiratory detection method, when the correlation coefficient showing the highest value is less than or equal to a preset threshold value, the basic signal extracted in the basic signal extraction step is replaced with a new template signal. As a template signal adding step of additionally recording in the recording means, in the residual signal generating step, when the correlation coefficient showing the highest value is less than or equal to a preset threshold value, the basic signal extracting step The residual signal may be generated by subtracting the new template signal additionally recorded in the template signal adding step from the basic signal extracted in.

Further, in the above-mentioned respiration detection program, the control unit, when the correlation coefficient showing the highest value is equal to or less than a preset threshold value, the basic signal extracted by the basic signal extraction function. In the case where the template signal adding function for additionally recording in the recording means as a new template signal is realized and the correlation coefficient showing the highest value in the residual signal generating function is equal to or less than a preset threshold value. The residual signal may be generated by subtracting the new template signal additionally recorded by the template signal adding function from the basic signal extracted by the basic signal extracting function.

The case where the correlation coefficient showing the highest value is less than or equal to the preset threshold value means that the basic signal extracted from the electrocardiogram data of the subject and the template signal recorded in the recording means have the highest similarity. Means that the template signal having a high value does not satisfy the correlation assumed in advance. In such a case, even if the template signal is subtracted from the basic signal extracted from the electrocardiogram data of the subject, the PQRST wave component of the electrocardiogram data cannot be sufficiently removed.

Therefore, when the correlation of the preset threshold is not satisfied, the basic signal that does not satisfy the correlation is additionally recorded in the recording means as a new template signal. Then, by subtracting the added new template signal from the basic signal extracted from the electrocardiogram data, it becomes possible to more effectively remove the PQRST wave component from the electrocardiogram data.

In addition, the newly added template signal is likely to have a waveform that is not similar to other template signals already recorded in the recording means. Therefore, it becomes possible to prepare template signals corresponding to various waveforms. Furthermore, the newly added template signal can be used for the process of removing the waveform of the PQRST wave whose waveform is disrupted by noise or the waveform of the PQRST wave due to arrhythmia such as early ventricular contraction.

Further, the above-described breathing detection device detects an amplitude value from the end point of the P wave of the basic signal extracted by the basic signal extraction means to the start point of the QRS wave, and based on the standard deviation of the amplitude value. , A threshold value determining means for determining a threshold value used for noise determination of the residual signal, and among the amplitude values of the residual signal, a median filter for an amplitude value that does not fall within the threshold value determined by the threshold value determining means. By applying the noise reduction means for reducing the noise to the residual signal, the respiratory wave calculation means, by using the residual signal noise reduced by the noise reduction means, the respiratory wave May be calculated.

Furthermore, the above-mentioned breath detection method detects the amplitude value from the end point of the P wave of the basic signal extracted in the basic signal extraction step to the start point of the QRS wave, and based on the standard deviation of the amplitude value. A threshold value determining step of determining a threshold value used for noise determination of the residual signal, and an amplitude value of the residual signal that does not fall within the threshold value determined in the threshold value determining step, with a median filter. By applying a noise reduction step of reducing the noise to the residual signal, in the respiratory wave calculation step, the respiratory signal using the residual signal subjected to noise reduction in the noise reduction step. May be calculated.

Further, in the above-mentioned respiration detection program, the control means is caused to detect an amplitude value from the end point of the P wave of the basic signal extracted by the basic signal extraction function to the start point of the QRS wave, Based on the standard deviation of the amplitude value, a threshold value determining function for determining a threshold value used for noise determination of the residual signal, and an amplitude value of the residual signal that does not fall within the threshold value determined by the threshold value determining function. By applying a median filter to the value, a noise reduction function for reducing the noise to the residual signal is provided, and in the respiratory wave calculation function , the noise is reduced by the noise reduction function. The respiration wave may be calculated using the residual signal.

In the basic signal extracted from the electrocardiogram data, the timing from the end point of the P wave to the start point of the QRS wave corresponds to the timing from the excitation (contraction) of the atrium to the excitation (contraction) of the ventricle, so that the movement of the heart Less is. Therefore, the amplitude value detected in this section tends to have a small amount of detection/variation. If an amplitude value (voltage value) outside a certain range is detected in a section where the detected value/variation amount of the amplitude value (voltage value) is small, it may be estimated that the amplitude value contains some noise. it can.

Therefore, in the respiration detection device, the respiration detection method, and the respiration detection program according to the present invention, the noise of the residual signal is based on the standard deviation of the amplitude value from the end point of the P wave of the basic signal to the start point of the QRS wave. The threshold used for the determination is determined. This makes it possible to more effectively and highly accurately determine the amplitude value that is highly likely to correspond to noise.

Further, the median filter is applied to the amplitude value of the residual signal that does not fall within the determined threshold value. By applying the median filter to reduce the noise with respect to the residual signal, it becomes possible to remove the local noise without a sense of discomfort while maintaining the accuracy of the signal waveform of the residual signal as a whole.

In the respiration detection device, the respiration detection method, and the respiration detection program according to the present invention, the template signal is subtracted from the basic signal including the PQRST wave for one heartbeat detected by the subject, so that the PQRST wave is extracted from the electrocardiogram data. The component of is removed. As in the breath detection apparatus according to the present invention, by generating a template signal based on the PQRST wave of the basic signal extracted from the same subject, the waveform of the template signal and the PQRST extracted from the electrocardiogram data are generated. The waveforms of the waves are similar to each other. Therefore, by subtracting the PQRST wave of the electrocardiogram data detected along with the pulsation of the heart of the subject by the PQRST wave component of the template signal generated by the same subject, the PQRST wave is effectively obtained from the electrocardiogram data. It becomes possible to remove the wave component.

Further, in the respiration detection device, the respiration detection method, and the respiration detection program according to the present invention, the difference between the amplitude values in which the residual signal changes at regular time intervals is obtained as a difference, and the change in the obtained difference is cumulatively obtained. Thus, a respiratory wave indicating the respiratory state of the subject is calculated. In this way, by obtaining the difference in amplitude change (difference in amplitude value) of the movement of the muscles associated with the breathing action and cumulatively obtaining the change in the difference, a breathing action having less amplitude change than the PQRST wave component is obtained. It is possible to visualize the accompanying movement of muscles. The respiratory rate of the subject can be detected from the electrocardiogram data by obtaining the waveform change per unit time based on the respiratory wave in which the movement of the muscle during the breathing motion is revealed.

1 is a block diagram showing a schematic configuration of a respiratory detection device according to an embodiment. 6 is a flowchart showing the processing contents of the CPU according to the embodiment. 3 is a flowchart showing in detail a part of the processing shown in FIG. 2. 4 is a flowchart showing in detail a part of the processing shown in FIG. 3. (A) is a figure which showed an example of the received electrocardiogram data, (b) is a figure which showed the start point P1 of P wave and the end point P2 of T wave. (A) to (c) show the correlation coefficient of the correlation coefficient while gradually shifting the time position of the Rb point in the PQRST wave component of the i-th template signal with reference to the Ra point of the PQRST wave included in the current basic signal. It is a figure for demonstrating the process which calculates. It is a figure for demonstrating the process which synthesize|combines the present basic signal and the kth template signal with the highest correlation coefficient, and produces|generates a new template signal. (A) is a figure showing a change state of the amplitude value of a residual signal, (b) is a figure showing threshold S1 and threshold S2 with respect to (a), and (c) is ( It is the figure which showed the signal which applied the median filter to the residual signal shown to a). (A) is a diagram showing a residual signal subjected to Hilbert transform, and (b) is a diagram showing a respiratory wave z1. (A) is a figure which showed difference y (m) of a residual signal, (b) is a figure which showed the absolute value of difference y (m), (c) shows respiratory wave z2. It is a figure. (A) shows a respiratory wave calculated using the differential integration method, and (b) is a waveform showing the respiratory state of the subject detected by the thermistor sensor at the same timing as (a). is there. Further, (c) shows a respiratory wave calculated using the Hilbert transform, and (d) is a waveform showing the respiratory state of the subject detected by the thermistor sensor at the same timing as (c). .. A case where a subject breathes using a respiratory load device that causes a load on inspiration in the upper respiratory tract, (a) shows a waveform of the subject's electrocardiogram, and (b) shows a respiratory band. The waveform of the respiratory signal detected using the sensor is shown, and (c) is a diagram showing the respiratory wave detected based on the electrocardiogram. (A) shows the differential signal calculated|required from the electrocardiogram data of the subject of central sleep apnea syndrome, (b) is the figure which showed the respiratory wave. It is the figure which showed the PQRST wave of general electrocardiogram data.

Hereinafter, an example of the breathing detection apparatus according to the present invention will be shown and described in detail with reference to the drawings. FIG. 1 is a block diagram showing a schematic configuration of a respiratory detection device 1, which is an example of a respiratory detection device according to the present invention. The respiratory detection device 1 includes a CPU (control means, basic signal extraction means, residual signal generation means, respiratory wave calculation means, respiratory rate detection means, template signal extraction means, template signal generation means, template signal update means, template signal addition means. , A threshold value determining means, a noise reducing means) 10, a data receiving section 12, a ROM (Read-only memory) 14, a RAM (Random-access memory, recording means) 16, and a display section 18. Further, the breathing detection apparatus 1 can read the electrocardiogram data (electrocardiogram waveform) detected by the general electrocardiogram detection apparatus 20 such as a Holter electrocardiograph in real time. Therefore, the respiratory detection device 1 can also be used as an optional product of the general electrocardiogram detection device 20.

The data receiving unit 12 continuously receives (acquires) the electrocardiogram data detected by the electrocardiogram detection device 20. The data receiving unit 12 receives the electrocardiogram data with the electrocardiogram detection device 20 via a wired cable or by using wireless communication. The electrocardiogram data received by the data receiving unit 12 is recorded in the RAM 16.

The ROM 14 stores in advance a program indicating the processing content of the CPU 10, parameters used for the processing, and the like (for example, a threshold value of a correlation coefficient described later). Further, the RAM 16 functions as a work area temporarily used for the processing of the CPU 10, and temporarily stores various setting values and data.

The display unit 18 is a display unit for visually showing a respiratory wave calculated based on the CPU 10 (for example, FIGS. 11A and 11C described later) and the like. As the display unit 18, it is possible to use a liquid crystal display or the like which is a general display means.

The CPU 10 calculates a respiratory wave from the electrocardiogram data received from the electrocardiogram detection device 20 based on a program recorded in the ROM 14 (for example, a program for the flowcharts shown in FIGS. 2 to 4 described later), and obtains the respiration rate. Perform processing.

2 to 4 are flowcharts showing the processing contents of the CPU 10. The CPU 10 detects the respiration rate from the electrocardiogram data based on the flowcharts shown in FIGS. Note that the processing contents based on the flowcharts shown in FIGS. 2 to 4 are recorded in the ROM 14 as a program, and the CPU 10 executes the processing shown in FIGS. 2 to 4 based on the program read from the ROM 14. Run.

FIG. 2 is a flowchart showing the main processing for detecting the respiratory rate in the CPU 10. As shown in FIG. 2, the CPU 10 receives the electrocardiogram data from the electrocardiogram detection device 20 via the data receiving unit 12 (S.01). As already described with reference to FIG. 14, the electrocardiogram data is data in which the waveform (amplitude change) of one heartbeat including the P wave, the QRS wave, and the T wave is continuously shown according to the heartbeat. The CPU 10 continuously receives the electrocardiogram data to acquire the electrocardiogram data in real time. FIG. 5A is a diagram showing an example of the received electrocardiogram data. As shown in FIG. 5B, the electrocardiogram data shown in FIG. 5A includes continuously received PQRST waves for three heartbeats.

Next, the CPU 10 detects the section points of the PQRST wave from the acquired electrocardiogram data (S.02). Specifically, the CPU 10 detects the peak portion of the QRS wave from the electrocardiogram data, and uses the amplitude value of the R point of the detected peak portion as a reference to determine the start point of the P wave, the end point of the T wave, the start point of the QRS wave, and the like. Ask. A specific detection method is a non-patent document ``Jiapu Pan and WJ Tompkins, A Real-Time QRS Detection Algorithm, IEEE Transaction on Biomedical Engineering, Vol. BME-32, No. 3, March 1985, pp.230- 236'', ``PS Hamilton and WJ Tompkins, Quantitative Investigation of QRS Detection Rules Using the MIT/BHI Arrhythmia Database, IEEE Transaction on Biomedical Engineering, Vol. BME-33, No. 12, Dec. 1986, pp.1157-1165''. , ``P Laguna, R. Jane, P. Caminal., Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database, Computers and Biomedical Research, Vol. 27, Issue 1, February 1994, pp.45-60. , Etc., and detailed description thereof is omitted here.

FIG. 5B shows, as an example, the start point P1 of the P wave and the end point P2 of the T wave for the electrocardiogram data shown in FIG. The CPU 10 detects the division points of the P wave, QRS wave and T wave from the waveform of the electrocardiogram data. By detecting the division points, it becomes possible to obtain the generation timing (occurrence time) of the P wave, QRS wave, and T wave included in the electrocardiogram data for one heartbeat, with the start point of the P wave as a reference.

Then, the CPU 10 determines the signal length including the PQRST wave for one heartbeat from the electrocardiogram data based on the detection of the division points, and continuously uses the signal including the PQRST wave for one heartbeat as a basic signal to continuously output the electrocardiogram data. Extract (S.03). The extracted basic signal is sequentially recorded in the RAM 16.

Next, the CPU 10 determines a threshold for noise level determination (a threshold used for noise determination) based on the amplitude value (voltage value) of the basic signal from the end point of the P wave to the start point of the QRS wave (S.04). .. Since the timing from the end point of the P wave to the start point of the QRS wave corresponds to the timing from after excitation (contraction) of the atrium to before excitation (contraction) of the ventricle, there is little movement of the heart. Therefore, as shown in FIG. 14, the amplitude value detected in this section tends to have a small detection amount/variation amount. When an amplitude value (voltage value) outside the fixed range (smaller than a negative fixed value or larger than a positive fixed value) is detected in a section where the detected amount/variation of the amplitude value (voltage value) is small , It can be estimated that the amplitude value contains some noise.

The CPU 10 finely detects the amplitude value (voltage value) from the end point of the P wave to the start point of the QRS wave, and calculates the standard deviation based on the plurality of detected amplitude values (voltage values). The CPU 10 according to the present embodiment determines the noise level determination threshold value range (threshold value S2, threshold value S1) based on this standard deviation. In the present embodiment, as an example, a preset a is used to determine an amplitude value range (voltage value range) from −a×standard deviation (threshold value S2) to +a×standard deviation (threshold value S1) as a noise level. Of the threshold range (threshold value S2, threshold value S1). The value of a is not particularly limited, but an integer value can be used, for example. a is recorded in the ROM 14 in advance.

The CPU 10 determines that the amplitude value includes noise when an amplitude value (voltage value) exceeding the range from −a×standard deviation (threshold value S2) to +a×standard deviation (threshold value S1) is detected. To do. The CPU 10 records the obtained threshold S1 and threshold S2 for noise level determination in the RAM 16.

Next, the CPU 10 performs a process of removing the PQRST wave component from the electrocardiogram data (S.05). FIG. 3 is a flowchart showing the processing contents of the CPU 10 for removing the PQRST wave component.

First, the CPU 10 executes the S. The first basic signal including the PQRST wave for one heartbeat extracted in the process of 03 is recorded in the RAM 16 as the first template signal (S.11). The first basic signal recorded in the RAM 16 by this processing (S.11) becomes the first template signal.

The template signal recorded in the RAM 16 is not limited to one. In the process described later, the CPU 10 performs a process of recording the signal of the PQRST wave satisfying a predetermined condition in the RAM 16 as a template signal (for example, S.26, S.29 in FIG. 4 described later). Therefore, the template signal recorded in the RAM 16 may be plural. The recorded template signal update processing (S.27 in FIG. 4) or the new (second and subsequent) template signal addition processing (S.29 in FIG. 4) will be described later.

Next, the CPU 10 performs a process of subtracting the template signal recorded in the RAM 16 from the first basic signal recorded in the RAM 16 (a signal including the PQRST wave for one heartbeat of the electrocardiogram data) (S.12). The component of the PQRST wave in the electrocardiogram data is removed by subtracting the template signal from the basic signal of the electrocardiogram data.

Next, the CPU 10 determines whether or not the next basic signal (a signal including the PQRST wave for the next one heartbeat) is extracted based on the electrocardiogram data received via the data receiving unit 12 (S). .13). When the next basic signal cannot be extracted (No in S.13), for example, when the detection processing of the electrocardiogram data in the subject is completed, etc., and the electrocardiogram data is not received via the data receiving unit 12, The CPU 10 ends the processing shown in FIG. The processing shifts to 06. When the next basic signal is extracted (Yes in S.13), the CPU 10 executes the template signal update addition process (S.14) shown in FIG. 13, the determination process (S.13) as to whether or not the next basic signal has been extracted is repeatedly executed.

FIG. 4 is a flowchart showing the updating/adding process of the template signal. When the next basic signal is extracted (Yes in S.13 of FIG. 3), the CPU 10 records the extracted next basic signal in the RAM 16 as the current basic signal (S.21).

Then, the CPU 10 calculates the correlation coefficient between the waveform of the recorded current basic signal and the waveform of the template signal recorded in the RAM 16 (S.22). Specifically, when n template signals are recorded in the RAM 16, the CPU 10 reads the template signals from the RAM 16 in order from the first template signal. Then, the CPU 10 compares the current waveform of the basic signal with the waveform of the i-th (i=1, 2,..., N) template signal to calculate the correlation coefficient.

When calculating the correlation coefficient, the CPU 10 refers to the Ra point of the PQRST wave included in the current basic signal as a reference, in the PQRST wave of the i-th template signal, as shown in FIGS. The correlation coefficient is calculated at a plurality of time positions while gradually shifting the time position of the Rb point during 2w time from before w time to after w time.

Even with the same electrocardiographic data of the subject, the waveforms of the PQRST waves detected for each heartbeat do not all have the same waveform. For example, when the heartbeat is fast or when an arrhythmia occurs, not only the length of the PQRST wave from the beginning to the end, but also the interval between the P wave, the QRS wave, the T wave, and the like may change. Therefore, the best correlation coefficient can be calculated by calculating a plurality of correlation coefficients while shifting the time position little by little during the 2w time.

In the respiratory detection device 1 according to the present embodiment, the time position where the best correlation coefficient is calculated is t (-w<t<+w). The above-mentioned time w may be a time of w>0 or more, and for example, time such as 0.05 seconds×sampling rate can be set as an example. The setting of the time w is not limited to a specific value, and various values can be used.

Next, the CPU 10 obtains the correlation coefficient having the highest value and the time position t when the correlation coefficient is calculated for each of the template signals from the 1st to the n-th template signal. (S.23). That is, n correlation coefficients and n time positions t are obtained.

Then, the CPU 10 extracts only one correlation coefficient having the highest value out of the n correlation coefficients, and calculates the extracted correlation coefficient (this template signal is referred to as the k-th template). Signal) and the time position tk at which the extracted correlation coefficient is calculated (S.24). That is, S. 23 and S.I. By the processing of 24, the correlation coefficient having the highest value with respect to the current basic signal, the k-th template signal for which the correlation coefficient is obtained, and the time position tk at which the correlation coefficient becomes the highest are obtained. You can

Next, the CPU 10 determines whether or not the obtained correlation coefficient with the highest value is larger than a preset threshold value (S.25). This threshold is a value recorded in advance in the ROM 14 or the like. Generally, the value of the correlation coefficient is in the range of +1 to -1. In the present embodiment, any value in the range of 0.75 to 0.90 is set as the threshold value.

When the correlation coefficient with the highest value is larger than the preset threshold value (Yes in S.25), the CPU 10 creates a new one based on the k-th template signal showing the highest correlation coefficient. A new template signal is generated (S.26). FIG. 7 is a diagram illustrating a process of generating a new template signal. A new template signal is generated by synthesizing the current basic signal and the k-th template signal showing the highest correlation coefficient. Specifically, composition is performed using the update coefficient α. The update coefficient α is a constant in the range of 0<α<1.

The CPU 10 adds the signal obtained by multiplying the current basic signal by the update coefficient α and the signal obtained by multiplying the k-th template signal by the coefficient (1-α) to each other to obtain a new signal. A simple template signal. When the addition is performed, the time position of each PQRST wave component is adjusted to the time position where the Ra point of the current basic signal and the Rb point of the kth template signal match. In the present embodiment, α=0.1 is used as an example of the value of the update coefficient α.

Then, the CPU 10 replaces the generated new template signal with the “kth template signal before combining” recorded in the RAM 16 and records it in the RAM 16. That is, the CPU 10 performs a process of updating (replacing) the k-th template signal recorded in the RAM 16 with the newly generated template signal (S.27).

Next, the CPU 10 adjusts the time position of the updated new template signal to the time position tk, and then subtracts the updated new template signal from the current basic signal (S.28). In the CPU 10 according to the present embodiment, before performing the subtraction process, the updated new template signal is subjected to the least squares regression process with the current basic signal, and the amplitude value width (voltage value width) of the new template signal is ) Adjustment. By performing the subtraction processing after the adjustment, the CPU 10 removes the waveform components of the P wave, QRS wave, and T wave from the electrocardiogram data (basic signal that is continuously extracted) (hereinafter referred to as residual signal). Ask. The CPU 10 records the obtained residual signal in the RAM 16. Then, the CPU 10 executes the S.S.T. Move to 13.

On the other hand, when the highest correlation coefficient is not larger than the preset threshold value (No in S.25), the CPU 10 uses the data of the current basic signal as a new template signal (n+1th). The template signal) is added to the RAM 16 (S.29).

When the correlation coefficient is not larger than the threshold, it means that the waveform of the k-th template signal showing the highest correlation coefficient and the waveform of the current basic signal are not similar to the expected one. Means That is, it is considered that the waveforms are different from each other or the amplitude values change almost opposite to each other (when the correlation coefficient is negative). When the correlation between the two signals is low as described above, for example, the amplitude of the P wave, QRS wave, or T wave in the current basic signal, the interval between the waveforms, and the wavelength are the kth template. It is considered to be very different from the signal.

In this way, although the correlation coefficient is the highest value and the k-th template signal that has a high possibility of resembling the waveform of the current basic signal is extracted, the correlation is higher than the preset threshold value. The number may be low. In this way, when the waveform of the current basic signal is likely to be dissimilar to the waveform of the template signal, the current basic signal is additionally recorded in the RAM 16 as a new template signal. By additionally recording a basic signal that is dissimilar to the waveform of the template signal in the RAM 16 as a new template signal, the amplitudes of the P wave, QRS wave, and T wave, which are difficult to be sufficiently removed by the conventional template signal, The new template signal enables effective reduction.

The CPU 10 sends the S. By subtracting the newly added template signal in 29, a signal obtained by removing the waveform components of the P wave, QRS wave and T wave from the current template signal is obtained and recorded in the RAM 16 (S.30).

Further, the number of template signals already recorded in the RAM 16 is recorded as a parameter n. Therefore, when a new template signal is added to the RAM 16 (S.29), the CPU 10 updates the value of the parameter n recorded in the RAM 16 to n+1 (S.31), and the process shown in FIG. ． Move to 13.

Next, the CPU 10 determines the S.V. The amplitude value (voltage value) exceeding the threshold for noise level determination determined in the processing of 04 is obtained, and processing for reducing the amplitude value exceeding the threshold is performed (S.06 shown in FIG. 2).

S. As described in the processing of 04, the CPU 10 determines −a×standard deviation (threshold value S2) and +a×standard deviation (threshold value S1) as threshold values for noise level determination. Therefore, when detecting an amplitude value whose amplitude value exceeds the value of a×standard deviation (threshold value S1), the CPU 10 determines that the amplitude value includes noise. Similarly, when the CPU 10 detects an amplitude value smaller than the value of −a×standard deviation (threshold value S2), it determines that the amplitude value contains noise. Then, the CPU 10 applies the median filter to the residual signal determined to contain noise. By applying the median filter, the amplitude value determined to include noise is reduced (corrected) to an intermediate value of the amplitude values detected at the timing (time position) before and after the noise. By reducing the value of the amplitude value in this way, the amplitude value of the residual signal is limited to the range of the threshold value S2 (−a×standard deviation) to the threshold value S1 (a×standard deviation), and Noise is removed.

FIG. 8A is a diagram showing a change state of the amplitude value (voltage value) of the residual signal for 5 seconds as an example. Further, FIG. 8B is a diagram showing a threshold value S1 on the plus side of the amplitude and a threshold value S2 on the minus side of the residual signal shown in FIG. 8A. 8C is a diagram showing the residual signal from which noise has been removed by applying the median filter to the residual signal shown in FIG. 8A.

Next, the CPU 10 performs a process of calculating a respiratory wave based on the residual signal from which noise has been removed (S.07). Since the residual signal is a signal obtained by removing the PQRST wave component from the electrocardiogram data, the amplitude component (voltage component) that changes with the pulsation of the heart is not observed. On the other hand, the residual signal includes an amplitude component (voltage component, EMG data component) of the movement of the muscle that changes with the breathing motion. However, since the amount of change in the amplitude component (voltage component) of muscle movement that changes with breathing motion is small, even if you observe the amplitude change of the residual signal as it is, the amplitude of muscle movement that changes with breathing motion. It is difficult to observe the components. Therefore, the CPU 10 obtains the respiratory wave by obtaining the difference between the amplitude values of the residual signal at constant time intervals as a difference and cumulatively obtaining the change state of the difference between the amplitude values. The respiratory wave is a signal indicating a change in the amplitude (voltage) of the electromyographic component associated with the respiratory action.

For example, by applying the Hilbert transform or the differential integration method to the residual signal from which noise has been removed, the change state of the amplitude value difference (voltage value difference) for each time can be cumulatively obtained.

When the respiratory wave is calculated using the Hilbert transform, the waveform of the residual signal is x, and the function obtained by applying the Hilbert transform to the residual signal is y.

y=Hilbert(x)... Formula 1
At this time, the respiratory wave z1 is
z1=(y*y+x*x) ^0.5 ...Equation 2
Can be found at. Here, “*” shown in Expression 2 means convolution integral.

FIG. 9A is a diagram showing the residual signal subjected to the Hilbert transform. FIG. 9B is a diagram showing the respiratory wave z1 obtained based on the equation 2. In the respiratory wave z<b>1, the peaked portion indicates the inspiratory movement and the valleyd portion indicates the expiratory movement.

When calculating the respiratory wave using the difference integration method, first, the difference y of the residual signal x is calculated,
y(m)=x(m+1)-x(m-1)... Formula 3
The respiratory wave z2 is obtained by obtaining the absolute value of the obtained difference y(m) and calculating the time integral of the absolute value.

z2(m)=sum(abs(y(i)*wf(j))... Formula 4
However, i=m−u/2,..., M+u/2
j=-u/2,...,u/2,
wf indicates a window function (for example, a Hanning window is used), and u indicates the length of the window function.

FIG. 10A is a diagram showing the difference y(m) of the residual signals obtained based on the equation 3, and FIG. 10B is a diagram showing the absolute value of the difference y(m). is there. Further, FIG. 10C is a diagram showing the respiratory wave z2 obtained based on the equation 4. Similar to the respiratory wave z1, the respiratory wave z2 shows an inspiratory operation in a portion where the amplitude value is a peak, and an expiratory operation in a portion where the amplitude value is a valley.

As described above, by obtaining the respiratory wave based on the electromyogram data during respiration included in the residual signal, it becomes possible to obtain the expiratory motion and the inspiratory motion from the electrocardiogram data. FIGS. 11A to 11D show the waveform of the respiratory wave obtained from the electrocardiogram data and the waveform of the respiratory action actually detected by using the thermistor sensor. (A) shows the respiratory wave calculated using the differential integration method, (b) shows the respiratory state of the subject detected using the thermistor sensor at the same timing as the respiratory wave of (a). It is a waveform. Further, (c) shows a respiratory wave calculated using the Hilbert transform, and (d) shows the respiratory state of the subject detected by the thermistor sensor at the same timing as the respiratory wave of (c). It is a waveform.

Comparing FIG. 11A and FIG. 11B, the undulation changes of the respective waveforms are substantially the same. Therefore, it can be determined that the timing of exhalation and inhalation of the breathing operation correspond to each other in FIGS. 11(a) and 11(b). Similarly, comparing FIG. 11(c) and FIG. 11(d), the changes in the undulations of the respective waveforms are substantially the same, and the expiration and inspiration timings of the respiratory action are shown in FIG. 11(c). It can be determined that they correspond with d). As is clear from FIGS. 11A to 11D, the expiration and inspiration timing of the respiratory wave calculated based on the electrocardiogram data is detected by a respiratory detection device such as a thermistor sensor that has been conventionally used. It corresponds to the timing. A respiratory wave can be obtained with high accuracy based on electrocardiogram data.

Next, the CPU 10 detects the respiratory rate based on the obtained respiratory wave (S.08). Specifically, the CPU 10 obtains the average value of the amplitude (voltage) in a certain period of time based on the amplitude change (voltage change) of the respiratory wave. The CPU 10 records the obtained average value in the RAM 16 as an amplitude threshold value. For example, in order to obtain the amplitude threshold value, the initial time T is recorded in advance in the ROM 14, and the CPU 10 reads the value of the initial time T from the ROM 14 and calculates the average value of the initial time T to obtain the amplitude threshold value.

Next, the CPU 10 uses the calculated amplitude threshold as a reference, and when the amplitude value (voltage value) of the respiratory wave exceeds the amplitude threshold from below to above (from an amplitude value lower than the average value to an average value lower than the average value). The timing of changing to a high amplitude value) is counted as a characteristic point of respiration. Then, the CPU 10 obtains the breathing rate per minute (per unit time) by counting the number of characteristic points for a certain period, for example, every 30 seconds or every 1 minute.

As the amplitude threshold, the average value of the initially calculated amplitudes can be used as it is as the amplitude threshold. On the other hand, for example, the average value may be updated by recalculating the average value of the time T every second or every fixed period of time.

Further, for example, by limiting the range of the determined amplitude threshold to an amplitude range of ¼ of the standard deviation of the respiratory wave, even if the amplitude value is exceptionally disturbed, the amplitude range is set to an appropriate amplitude range. It is possible to modify the amplitude threshold so that

On the other hand, if the determined new amplitude threshold does not fall within the amplitude range of ¼ of the standard deviation of the respiratory wave, a new amplitude threshold may be calculated and changed. For example, the amplitude range of 1/4 of the standard deviation calculated by the old amplitude threshold is set to 80%, the amplitude range of 1/4 of the standard deviation calculated by the new amplitude threshold is set to 20%, and a new amplitude range is set. You may do so. The CPU 10 repeatedly executes the detection of the characteristic points in the respiratory wave until the electrocardiogram data cannot be received from the electrocardiogram detection device 20.

As described above, the CPU 10 of the respiratory detection device 1 according to the present embodiment generates/updates the template signal based on the basic signal of the subject extracted in the past from the electrocardiogram data, and generates/updates the template signal. Based on the template signal, the PQRST wave component is removed from the electrocardiogram data of the same subject. By removing the PQRST wave component from the electrocardiogram data using the template signal obtained from the same subject, the waveform of the electrical signal associated with the heartbeat can be effectively removed from the electrocardiogram data.

Further, the CPU 10 reduces noise based on the amplitude value from the end point of the P wave to the start point of the QRS wave. By this reduction processing, it becomes possible to effectively reduce noise with reference to the timing when the amplitude of the electric signal accompanying the heartbeat is difficult to be detected.

Further, the CPU 10 of the respiratory detection device 1 according to the present embodiment obtains the difference between the amplitude values (voltage values) at regular time intervals based on the residual signal, and cumulatively obtains the change state of the difference. Specifically, by applying the Hilbert transform (for example, Equation 1, Equation 2) or applying the differential integration method (for example, Equation 3, Equation 4) to the residual signal, Ask for. By obtaining the respiratory wave, the CPU 10 can obtain the expiratory motion and the inspiratory motion of the subject based on the electrocardiogram data.

Furthermore, the CPU 10 counts the characteristic points of respiration per 30 seconds or 1 minute based on the average value of the amplitude of the respiratory wave. This makes it possible to obtain the respiratory rate per unit time based on the electrocardiogram data. In the breathing detection apparatus 1, since the respiration rate can be obtained based on the electrocardiogram, it is not necessary to attach a physical sensor to the nose or mouth like the breathing detection apparatus using the flow sensor or the thermistor sensor. Therefore, it is possible to reduce the burden of the examination on the subject and reduce the discomfort during the examination. Moreover, since it is not necessary to tighten the chest like a respiratory band sensor, the subject does not feel uncomfortable or restrained. Further, since it is not necessary to disinfect the sensor after using the sensor or remove the belt from the sensor, it is possible to reduce the use load and maintenance load of the device. Moreover, by using a Holter electrocardiogram or the like, electrocardiographic data can be continuously detected in daily life. Therefore, by obtaining the respiratory wave from the electrocardiogram data, it becomes possible to continuously examine the respiratory state of the subject in daily life.

Further, the template signal for removing the PQRST wave component is generated and updated based on the basic signal including the PQRST wave for each heartbeat detected from the electrocardiogram data of the same subject. Generally, the heartbeat interval (heart rate), the waveform of the PQRST wave, and the like are different for each subject. However, in the respiratory detection device 1 according to the present embodiment, since the template signal is generated/updated based on the basic signal of the subject, the template signal suitable for the subject can be generated.

Further, in the respiratory detection device 1, the correlation coefficient between the waveform of the PQRST wave in the basic signal and the waveform of the PQRST wave component in the template signal is obtained, and a new template signal is added/updated based on the correlation coefficient. .. Therefore, the height of the waveform in the PQRST wave component of the template signal, the interval between the waveforms, the wavelength, and the like can be corrected and added to be optimal for the subject. Therefore, by subtracting the new template signal from the basic signal, it becomes possible to effectively remove the PQRST wave component from the electrocardiogram data.

Further, in the respiratory detection device 1 according to the present embodiment, the Rb point of the PQRST wave component of the template signal is slightly shifted at the time position of ±w with reference to the Ra point of the PQRST wave of the basic signal, and the correlation coefficient To calculate. By calculating the correlation coefficient while shifting the positional relationship between the two PQRST wave components little by little in this way, a more optimal correlation coefficient can be obtained, and the PQRST wave component is effectively removed from the electrocardiogram data. It will be possible.

Further, in the respiratory detection device 1 according to the present embodiment, the template signal is subtracted from the electrocardiogram data (basic signal) in order to remove the PQRST wave component from the electrocardiogram data. Therefore, unlike the conventional respiratory detection device, a process of uniformly removing the amplitude of a certain band (for example, a band of 120 Hz or less) of the electrocardiogram data by the filter process is not performed. Therefore, the amplitude of the PQRST wave component in the electrocardiogram data can be effectively removed in the entire frequency band. Further, it is possible to prevent the fluctuation component of the electric signal associated with the breathing motion from being reduced from the electrocardiogram data.

In addition, the respiratory detection device 1 according to the present embodiment can detect the respiratory state based on the movement of the muscles associated with the respiratory action. Therefore, it is also possible to judge from the respiratory wave whether the breathing state of the subject is resting breathing or forced breathing.

Here, the forced breathing means a breathing performed by mobilizing a respiratory muscle which is not used in the resting breathing. Resting breathing is usually performed by contraction and relaxation of respiratory muscles such as the diaphragm and the external intercostal muscles. On the other hand, in forced breathing, auxiliary respiratory muscles such as the sternocleidomastoid muscle are mobilized during inspiration, and the internal intercostal muscles and abdominal muscles are activated during expiration to perform breathing. Forced breathing tends to occur during periods of severe hypoxemia and asthma.

Resting breathing and forced breathing have different mobilized muscles and the like, so that the waveforms of respiratory waves detected with the breathing motion are different. Therefore, an expert such as a doctor can easily determine whether the respiratory state is resting breathing or forced breathing by checking the waveform of the respiratory wave.

FIG. 12A shows a waveform of electrocardiogram data, FIG. 12B shows a waveform of a respiratory action detected using a respiratory band sensor, and FIG. 12C shows a respiratory wave detected based on the electrocardiographic data. Is shown. In FIG. 12, each signal was detected while the subject breathed using the respiratory load device that causes a load on the inspiration in the upper airway.

In FIGS. 12A to 12C, the inspiratory load is not imposed in the respiratory action before the time x1, and the inspiratory load is imposed in the respiratory action after the time x1. As shown in FIGS. 12A and 12C, after the inspiratory load is started, the baseline sway occurs in the electrocardiogram data compared to before the inspiratory load is started, and the amplitude value of the respiratory wave changes. It is getting bigger. As described above, when the baseline sway occurs in the electrocardiogram data and the amplitude of the respiratory wave increases, it can be determined that the forced respiration is performed.

Furthermore, the breathing state can be examined based on the muscle activity state during the breathing motion. Therefore, in recent years, it is possible to easily investigate the respiratory state during sleep by obtaining the respiratory wave of the patient for the sleep apnea syndrome that is increasing. The sleep apnea syndrome can be broadly classified into two types: obstructive sleep apnea syndrome and central sleep apnea syndrome. Obstructive sleep apnea becomes apnea when the upper respiratory tract does not have sufficient space for air to pass. Therefore, in the case of obstructive sleep apnea syndrome, the air passage of the upper respiratory tract is physically occluded, but the movement of the muscles accompanying the respiratory action is continuously performed.

On the other hand, in the case of central sleep apnea syndrome, the brain does not output a command regarding the respiratory action to the respiratory organs due to the abnormality of the brain. Therefore, in the central sleep apnea syndrome, although the air passage is not physically blocked, the movement of the muscles associated with the breathing movement is significantly reduced.

Therefore, for a patient with sleep apnea syndrome, the waveform of the respiratory wave is detected based on the electrocardiogram data while detecting the respiratory state from the nose and mouth using a flow sensor, a thermistor sensor, or the like. When the amplitude value of the respiratory wave obtained based on the electrocardiogram data does not change much, it is considered that the movement of the muscles for performing the breathing action is decreased, and therefore the central sleep apnea syndrome. It can be judged that On the other hand, when the amplitude value of the respiratory wave changes but the respiratory state from the mouth or nose is not detected by the flow sensor or the like, it can be determined that the obstructive sleep apnea syndrome is applicable.

FIG. 13 shows the differential signal (FIG. 13(a)) and respiratory wave (FIG. 13(b)) of a patient with central sleep apnea. 13A and 13B show a state in which the patient stops breathing at y1 and resumes breathing at the timing of y2. As shown in FIGS. 13(a) and 13(b), the amplitude change of the differential signal becomes weak from y1 to y2, and the respiratory wave shows a flat waveform. As described above, when the change in the amplitude value of the respiratory wave is weak and the waveform is flat (flat), it is determined that the movement of the muscle accompanying the respiratory action is weak and that the respiratory action is not performed. You can

Moreover, since respiratory system diseases such as sleep apnea syndrome are deeply related to arrhythmia, heart failure, etc., respiratory and heartbeat are often measured simultaneously in clinical practice. Therefore, it is possible to simultaneously measure the pulsation state and the respiratory state of the heart by using a conventional respiratory detection device such as a flow sensor and the electrocardiogram measurement together, or by detecting a respiratory wave from the electrocardiogram data. . Further, it becomes possible to detect the apnea state of the patient in more detail.

Further, in the respiratory detection device 1 according to the present embodiment, the time point of Rb of the template signal is set in the range from -w to +w with reference to the Ra point of the current basic signal detected based on the electrocardiogram data. The value of the correlation coefficient is obtained while shifting the position little by little. Therefore, even when the subject has an arrhythmia or the like and the timing of the heartbeat is not constant, it is possible to improve the accuracy of removing the PQRST wave component from the electrocardiogram data.

Further, the respiratory detection device 1 according to the present embodiment can detect the respiratory rate and the respiratory state by receiving the electrocardiogram data from the electrocardiogram detection device 20. Therefore, as the electrocardiogram detection device 20, a Holter electrocardiograph or the like which is generally commercially available can be used. By using the Holter electrocardiograph or the like which has already been sold, etc., as it is, only by adding the respiratory detection device 1 according to the present embodiment, not only the pulsating state of the heart of the subject but also the respiratory rate and the respiration It becomes possible to easily detect the operation state.

As described above, the respiratory detection device, the respiratory detection method and the respiratory detection program according to the present invention have been described in detail with reference to the drawings, but the respiratory detection device according to the present invention has the same contents as those shown in the present embodiment. Is not limited.

For example, as the respiratory detection device 1 according to the present embodiment, it is possible to use a general information terminal such as a smartphone or a tablet. It is also possible to receive the electrocardiogram data using the communication function of the information terminal and to detect the respiratory wave and the respiration rate from the electrocardiogram data by using the CPU, ROM, RAM, etc. of the information terminal. In this case, a general information terminal is installed in a respiratory detection program for executing the processing shown in FIGS. The same processing content as that of the breath detection device 1 can be realized by the information terminal.

Similarly, by installing a respiration detection program in a general personal computer or the like, the respiration detection apparatus 1 described in the present embodiment can be used in the same manner as the respiration detection device 1 using a highly versatile personal computer. It is possible to realize the processing content.

Furthermore, in the respiratory detection device 1 according to the present embodiment, the processing S. 25, it is determined whether the correlation coefficient is larger than the threshold value, and if the correlation coefficient is larger (Yes in S25), a new template signal is created (S.26), and the current basic The processing procedure for subtracting a new template signal from a signal to obtain a residual signal has been described. However, before determining whether the correlation coefficient is larger than the threshold value (S.25), the k-th template signal is subtracted from the current basic signal to obtain the residual signal, and then the correlation coefficient is larger than the threshold value. The configuration may be such that it is determined whether it is larger (S.25), a new template signal is created (S.26), and the new template signal is updated (S.27). Similarly, before determining whether the correlation coefficient is larger than the threshold value (S.25), the k-th template signal is subtracted from the current basic signal to obtain the residual signal, and then the phase relationship is determined. It is determined whether the number is larger than the threshold value (S.25), and when the correlation coefficient is not larger (No in S25), the current basic signal is added as a new template signal (S.29), It is also possible to adopt a configuration in which the value of the parameter n is changed to n+1 (S.31).

1... Respiration detection device 10... CPU (control means, basic signal extraction means, residual signal generation means, respiratory wave calculation means, respiration rate detection means, template signal extraction means, template signal generation means, template signal update means, template signal addition Means, threshold value determining means, noise reducing means)
12... Data receiving section 14... ROM
16... RAM (recording means)
18... Display 20... Electrocardiogram detection device

Claims (18)

Basic signal extracting means for continuously extracting a basic signal containing a PQRST wave for one heartbeat from the electrocardiogram data of the subject;
Recording means for recording the basic signal of the subject extracted in the past by the basic signal extracting means as a template signal;
Residual signal generation for generating a residual signal by removing the component of the PQRST wave from the electrocardiogram data by subtracting the template signal recorded in the recording means from the basic signal extracted by the basic signal extraction means Means and
Obtaining a difference in amplitude value of the residual signal for each constant time as a difference, and by cumulatively obtaining a change in the difference, a respiratory wave calculating means for calculating a respiratory wave indicating the respiratory state of the subject. ,
A respiratory rate detecting means for detecting the respiratory rate per unit time based on the change in the waveform of the respiratory wave. At least one or more template signals are recorded in the recording means,
Obtaining a correlation coefficient between the waveform of the basic signal extracted by the basic signal extracting unit and the waveform of the template signal recorded in the recording unit for all the template signals recorded in the recording unit. According to the above, the template signal extracting means for extracting only one template signal having the highest correlation coefficient is provided,
The residual signal generating means generates the residual signal by subtracting the template signal extracted only one by the template signal extracting means from the basic signal extracted by the basic signal extracting means. The breathing detection apparatus according to claim 1, wherein the breathing detection apparatus is used. The template signal extraction means obtains the correlation coefficient while gradually shifting the time position of the R point of the PQRST wave of the template signal with reference to the R point of the PQRST wave included in the basic signal,
The residual signal generating means, at the time position where the correlation coefficient shows the highest value, only one template extracted by the template signal extracting means from the basic signal extracted by the basic signal extracting means. The respiratory detection device according to claim 2, wherein the residual signal is generated by subtracting a signal. Template signal generation means for generating a new template signal by combining the basic signal extracted by the basic signal extraction means and the template signal extracted only one by the template signal extraction means;
A new template signal generated by the template signal generation means is replaced with the template signal extracted by only one, and the template signal update means for recording in the recording means is provided,
The residual signal generation means generates the residual signal by subtracting the new template signal generated by the template signal generation means from the basic signal extracted by the basic signal extraction means. The respiratory detection device according to claim 2 or claim 3. A template for additionally recording the basic signal extracted by the basic signal extracting means as a new template signal in the recording means when the correlation coefficient showing the highest value is equal to or less than a preset threshold value. Equipped with signal addition means,
The residual signal generating means, when the correlation coefficient showing the highest value is equal to or less than a preset threshold value, from the basic signal extracted by the basic signal extracting means, by the template signal adding means. The respiratory detection device according to claim 2, wherein the residual signal is generated by subtracting the newly recorded new template signal. An amplitude value between the end point of the P wave and the start point of the QRS wave of the basic signal extracted by the basic signal extracting means is detected, and used for noise determination of the residual signal based on the standard deviation of the amplitude value. Threshold determining means for determining the threshold,
Among the amplitude values of the residual signal, a noise reduction unit that reduces noise with respect to the residual signal by applying a median filter to the amplitude value that does not fall within the threshold value determined by the threshold value determination unit, Equipped with
The respiratory wave calculating means calculates the respiratory wave using the residual signal whose noise has been reduced by the noise reducing means. The respiratory detection device described. A basic signal extraction step of continuously extracting a basic signal including a PQRST wave for one heartbeat from the electrocardiogram data of the subject;
The basic signal of the subject extracted in the past in the basic signal extracting step is recorded as a template signal in a recording unit, and the template signal read from the recording unit is extracted in the basic signal extracting step. A residual signal generation step of generating a residual signal by removing the component of the PQRST wave from the electrocardiogram data by subtracting from the basic signal;
Obtaining a difference in amplitude value of the residual signal at constant time intervals as a difference, and by cumulatively obtaining a change in the difference, a respiratory wave calculating step of calculating a respiratory wave indicating the respiratory state of the subject. ,
A breathing rate detecting step of detecting a breathing rate per unit time based on the waveform change of the breathing wave. At least one or more template signals are recorded in the recording means,
The correlation coefficient between the waveform of the basic signal extracted in the basic signal extraction step and the waveform of the template signal read by the recording means is obtained for all the template signals recorded in the recording means. Thereby, a template signal extracting step of extracting only one template signal having a highest correlation coefficient,
In the residual signal generating step, the residual signal is generated by subtracting the template signal extracted only once in the template signal extracting step from the basic signal extracted in the basic signal extracting step. The respiratory detection method according to claim 7, which is characterized in that: In the template signal extraction step, with reference to the R point of the PQRST wave included in the basic signal, while gradually shifting the time position of the R point in the PQRST wave of the template signal, the correlation coefficient is obtained,
In the residual signal generating step, at the time position where the correlation coefficient shows the highest value, only one template is extracted in the template signal extracting step from the basic signal extracted in the basic signal extracting step. The respiratory detection method according to claim 8, wherein the residual signal is generated by subtracting a signal. A template signal generation step of generating a new template signal by combining the basic signal extracted in the basic signal extraction step and the template signal extracted only one in the template signal extraction step;
A new template signal generated in the template signal generation step is replaced with the template signal extracted by only one, and a template signal updating step of causing the recording means to record the template signal,
In the residual signal generating step, the residual signal is generated by subtracting the new template signal generated in the template signal generating step from the basic signal extracted in the basic signal extracting step. 10. The respiratory detection method according to claim 8 or claim 9. A template for additionally recording the basic signal extracted in the basic signal extraction step as a new template signal in the recording means when the correlation coefficient showing the highest value is equal to or less than a preset threshold value. With a signal addition step,
In the residual signal generation step, if the correlation coefficient showing the highest value is less than or equal to a preset threshold value, from the basic signal extracted in the basic signal extraction step, in the template signal addition step The respiratory detection method according to claim 8 or 9, wherein the residual signal is generated by subtracting the newly recorded new template signal. An amplitude value between the end point of the P wave and the start point of the QRS wave of the basic signal extracted in the basic signal extraction step is detected, and used for noise determination of the residual signal based on the standard deviation of the amplitude value. A threshold determination step of determining a threshold,
Among the amplitude values of the residual signal, a noise reduction step of reducing noise with respect to the residual signal by applying a median filter to the amplitude value that does not fall within the threshold value determined in the threshold value determination step, Equipped with
The respiratory wave is calculated in the breathing wave calculating step by using the residual signal whose noise is reduced in the noise reducing step. The respiratory detection method described. A respiratory detection program for a respiratory detection device for detecting the respiratory rate of the subject based on the electrocardiogram data of the subject,
The respiratory detection device has a recording means,
In the control means of the respiratory detection device,
A basic signal extracting function for continuously extracting a basic signal including a PQRST wave for one heartbeat from the electrocardiogram data,
The basic signal of the subject extracted in the past by the basic signal extraction function is recorded as a template signal in the recording means, and the template signal read from the recording means is processed by the basic signal extraction function. A residual signal generation function for generating a residual signal by removing the component of the PQRST wave from the electrocardiogram data by subtracting from the extracted basic signal,
Obtaining a difference in amplitude value of the residual signal for each constant time as a difference, and by cumulatively obtaining a change in the difference, a respiratory wave calculation function for calculating a respiratory wave indicating the respiratory state of the subject. ,
And a respiratory rate detecting function for detecting the respiratory rate per unit time based on the change in the waveform of the respiratory wave. At least one or more template signals are recorded in the recording means,
In the control means,
The correlation coefficient between the waveform of the basic signal extracted by the basic signal extracting function and the waveform of the template signal read by the recording unit is obtained for all the template signals recorded in the recording unit. As a result, a template signal extraction function for extracting only one template signal having the highest correlation coefficient is realized,
In the residual signal generation function, the residual signal is generated by subtracting only one template signal extracted by the template signal extraction function from the basic signal extracted by the basic signal extraction function. The respiratory detection program according to claim 13. With respect to the control means,
In the template signal extraction function, with reference to the R point of the PQRST wave included in the basic signal, while gradually shifting the time position of the R point in the PQRST wave of the template signal, the correlation coefficient is obtained,
In the residual signal generation function, only one template is extracted by the template signal extraction function from the basic signal extracted by the basic signal extraction function at the time position where the correlation coefficient shows the highest value. The respiratory detection program according to claim 14, wherein the residual signal is generated by subtracting a signal. In the control means,
A template signal generation function for generating a new template signal by combining the basic signal extracted by the basic signal extraction function and the template signal extracted only one by the template signal extraction function;
A new template signal generated by the template signal generation function is replaced with the template signal extracted by only one, and a template signal update function for recording in the recording means is realized,
In the residual signal generation function, the residual signal is generated by subtracting the new template signal generated by the template signal generation function from the basic signal extracted by the basic signal extraction function. 16. The respiratory detection program according to claim 14 or 15. In the control means,
A template for additionally recording the basic signal extracted by the basic signal extraction function as a new template signal in the recording means when the correlation coefficient showing the highest value is equal to or less than a preset threshold value. Realize the signal addition function,
In the residual signal generation function, when the correlation coefficient showing the highest value is less than or equal to a preset threshold value, from the basic signal extracted by the basic signal extraction function, by the template signal addition function The respiratory detection program according to claim 14 or 15, wherein the residual signal is generated by subtracting the new template signal additionally recorded. With respect to the control means,
An amplitude value between the end point of the P wave and the start point of the QRS wave of the basic signal extracted by the basic signal extraction function is detected, and used for noise determination of the residual signal based on the standard deviation of the amplitude value. A threshold determination function for determining the threshold,
Among the amplitude values of the residual signal, a noise reduction function that reduces noise with respect to the residual signal by applying a median filter to the amplitude value that does not fall within the threshold value determined by the threshold value determination function. With and
18. The respiratory wave calculation function calculates the respiratory wave using the residual signal whose noise has been reduced by the noise reduction function. The respiratory detection program described.

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