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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\u2010frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel\u2010based Chirplet transform (SCT) is used to evaluate the time\u2010frequency representation of PCG recording, and the features like L1\u2010norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time\u2010frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine\u2010 (ELM\u2010) 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\u2010out cross\u2010validation (CV) method. The proposed approach is applicable for the Internet of Things\u2010 (IoT\u2010) driven smart healthcare system for the accurate detection of HVAs.<\/jats:p>","DOI":"10.1155\/2020\/8843963","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T03:50:05Z","timestamp":1608609005000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time\u2010Frequency Representation of PCG Recordings"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2267-7314","authenticated-orcid":false,"given":"Samit Kumar","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9742-025X","authenticated-orcid":false,"given":"R. 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