Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals

• Published in: Computers in biology and medicine Vol. 118; p. 103632
• Main Authors: Ghosh, Samit Kumar, Ponnalagu, R.N., Tripathy, R.K., Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2020; Elsevier Limited
• Subjects: Abnormalities; Aorta; Aortic stenosis; Automation; Blood coagulation; Chirplet transform; Classification; Classifiers; Congestive heart failure; Coronary artery disease; Entropy; Frequency analysis; Heart valve diseases (HVDs); Heart valves; Internal Medicine; Multiclass composite classifier; Other; PCG; Regurgitation; Signal classification; Stenosis; Time–frequency analysis; Chirplet transform; Heart valve diseases (HVDs); Time–frequency analysis; Multiclass composite classifier; PCG
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.103632

Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time–frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications. •A new method based on the time–frequency analysis of PCG signal using Chirplet transform has been proposed.•The LEN and LENT features are evaluated from each frequency component of the time–frequency matrix of PCG signal.•A multiclass component classifier is formulated for the detection of HVDs.•The method demonstrated higher performance with an overall accuracy of 98.33%.