Roquemen-Echeverri et al., 2021 - Google Patents
An AI-powered tool for automatic heart sound quality assessment and segmentationRoquemen-Echeverri et al., 2021
- Document ID
- 3707146327613483856
- Author
- Roquemen-Echeverri V
- Jacobs P
- Heitner S
- Schulman P
- Wilson B
- Mahecha J
- Mosquera-Lopez C
- Publication year
- Publication venue
- 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
External Links
Snippet
Objective: To design an AI-powered tool to automatically assess the quality of phonocardiogram (PCG) recordings, and then identify S1 and S2 heart sounds using PCG recordings only. Methods We used PCG recordings from two datasets; a publicly available …
- 230000011218 segmentation 0 title abstract description 34
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0402—Electrocardiography, i.e. ECG
- A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/02—Stethoscopes
- A61B7/04—Electric stethoscopes
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L17/00—Speaker identification or verification
- G10L17/26—Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/003—Detecting lung or respiration noise
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4818—Sleep apnoea
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Clifford et al. | Recent advances in heart sound analysis | |
Baghel et al. | Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network | |
US12016705B2 (en) | Methods and systems for identifying presence of abnormal heart sounds of a subject | |
Singh et al. | Short PCG classification based on deep learning | |
Wang et al. | Phonocardiographic signal analysis method using a modified hidden Markov model | |
Deperlioglu | Heart sound classification with signal instant energy and stacked autoencoder network | |
Grønnesby et al. | Feature extraction for machine learning based crackle detection in lung sounds from a health survey | |
Singh et al. | Short unsegmented PCG classification based on ensemble classifier | |
Hazeri et al. | Classification of normal/abnormal PCG recordings using a time–frequency approach | |
Megalmani et al. | Unsegmented heart sound classification using hybrid CNN-LSTM neural networks | |
Putra et al. | Study of feature extraction methods to detect valvular heart disease (vhd) using a phonocardiogram | |
Bourouhou et al. | Heart sounds classification for a medical diagnostic assistance | |
Patwa et al. | Heart murmur and abnormal pcg detection via wavelet scattering transform & a 1d-cnn | |
Ajitkumar Singh et al. | An improved unsegmented phonocardiogram classification using nonlinear time scattering features | |
Talal et al. | Machine learning‐based classification of multiple heart disorders from PCG signals | |
Roquemen-Echeverri et al. | An AI-powered tool for automatic heart sound quality assessment and segmentation | |
Roy et al. | Recent Advances in PCG Signal Analysis using AI: A Review | |
Banerjee et al. | A robust dataset-agnostic heart disease classifier from phonocardiogram | |
Banerjee et al. | An irregularity measurement based cardiac status recognition using support vector machine | |
Panah et al. | Exploring composite dataset biases for heart sound classification | |
Altaf et al. | Systematic review for phonocardiography classification based on machine learning | |
Duggento et al. | Classification of real-world pathological phonocardiograms through multi-instance learning | |
Blitti et al. | Heart Sounds Classification Using Frequency Features with Deep Learning Approaches | |
Taneja et al. | Heart audio classification using deep learning | |
Naqvi et al. | Deep Learning Based Intelligent Classification of COVID-19 & Pneumonia Using Cough Auscultations |