Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features

In this letter, we propose a method for the automated detection of heart valve disorders namely, the aortic stenosis (AS), mitral stenosis (MS), and mitral regurgitation (MR) from the phonocardiogram (PCG) signal. The wavelet synchrosqueezing transform is used to obtain the time-frequency matrix fro...

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Bibliographic Details
Published inIEEE sensors letters Vol. 3; no. 12; pp. 1 - 4
Main Authors Ghosh, Samit Kumar, Tripathy, Rajesh Kumar, Ponnalagu, R. N., Pachori, Ram Bilas
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Automation
Disorders
Electrocardiography
Feature extraction
Heart
heart valve disorders
Heart valves
Pathology
PCG
Phonocardiography
Radio frequency
random forest classifier
Sensor signal processing
Time-frequency analysis
Training
Wavelet analysis
wavelet synchrosqueezing transform
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Summary:In this letter, we propose a method for the automated detection of heart valve disorders namely, the aortic stenosis (AS), mitral stenosis (MS), and mitral regurgitation (MR) from the phonocardiogram (PCG) signal. The wavelet synchrosqueezing transform is used to obtain the time-frequency matrix from the segmented cycles of the PCG signal. From the time-frequency matrix, the magnitude and phase features are extracted. The random forest (RF) classifier is used for the classification. The results reveal that the proposed method has the average individual accuracy (IA) values of 98.83%, 97.66%, 91.16%, and 92.83% for normal, AS, MS, and MR classes.
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content type line 14
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2019.2949170