Classification of the Third and Fourth Heart Sounds using Intrinsic Time-Scale Decomposition and Support Vector Machine Technique
Sai Bharadwaj B1, Ch. Sumanth Kumar2
1Sai bharadwaj B *, Department of Electronics and Communications, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India.
2Ch. Sumanth Kumar, Department of Electronics and Communications, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India.
Manuscript received on October 16, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 1172-1177 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4500119119/2019©BEIESP | DOI: 10.35940/ijitee.A4500.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The two diastolic heart sounds reflecting the malfunctionality of heart are third and fourth heartsounds(S3 and S4). Early detection of heart failures can decrease the risk by identifying the abnormal heart sounds through Phonocardiogram (PCG) signal analysis. In this paper abnormal heart sounds are identified and classified using Intrinsic time scale decomposition (ITD) and Support vector machine (SVM). The proposed framework has been tested on authenticated database signals under abnormal conditions. The success rate is really conquering for the SVM classifier with an accuracy over 94% in the S3 detection and 91% for the S4, which reveals the effectiveness and high efficiency of the proposed work
Keywords: PCG Signal, Intrinsic Timescale Decomposition, Support Vector Machine.
Scope of the Article: Classification