Abstract
The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.
Highlights
IntroductionThe heart valve ailments (HVAs) are cardiovascular abnormalities, and these ailments occur due to the defect in any of the valves (tricuspid, pulmonary, mitral, and aortic) of the heart [1, 2]
The heart valve ailments (HVAs) are cardiovascular abnormalities, and these ailments occur due to the defect in any of the valves of the heart [1, 2]
The intraclass variations of the LN features for 18th, 50th, 196th, 293th, and 378th frequency components for all N, mitral valve prolapse (MVP), aortic stenosis (AS), mitral regurgitation (MR), and mitral stenosis (MS) categories are depicted in Figures 5(a)–5(e), respectively
Summary
The heart valve ailments (HVAs) are cardiovascular abnormalities, and these ailments occur due to the defect in any of the valves (tricuspid, pulmonary, mitral, and aortic) of the heart [1, 2]. The AS pathology refers to the improper opening of the aortic valve, which prevents the flow of blood from the left ventricle to the aorta of the heart [5] [6]. For the diagnosis of these pathologies, different imaging techniques such as computed tomography scan, magnetic resonance imaging (MRI), cardiac echocardiography, and BioMed Research International ultrasonic devices have been used [7,8,9,10] It has been reported from the literature that various quantitative parameters such as transvalvular velocity, average value area, and mean value of transvalvular gradient have been considered to determine the progression of HVAs [11]. The development of new methods for the extraction of PCG signal features and the classification of HVAs is required
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