DETECTION OF HEART BEAT SOUND CONDITIONS USING LSTM AND MFCC

Suman Mukherjee , Muddana Vamsi Krishna , Shaik Abdul Kaleem , Thota Bharath Kumar

heart sound classification, cardiovascular diseases, Long Short Term Memory, MFCC, detection of heart sound, heart disease , deep learning. DFT

In this paper, we present a method for heart sound classification using long short-term memory (LSTM) and Mel Frequency Cepstral Coefficients (MFCC). The proposed method uses MFCC to extract features from heart sounds and LSTM to classify heart sounds into normal and abnormal classes. The dataset used in this study consists of heart sound recordings from different clinical settings, and the proposed method achieved an accuracy of 92% on this dataset.

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"DETECTION OF HEART BEAT SOUND CONDITIONS USING LSTM AND MFCC", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 5, page no.632 - 638, May-2023, Available :https://ijsdr.org/papers/IJSDR2305093.pdf

Volume 8 Issue 5, May-2023

Pages : 632 - 638

Paper Reg. ID: IJSDR_206083

Published Paper Id: IJSDR2305093

Downloads: 000347215

Research Area: Computer Science & Technology 

Country: Chennai, Tamil Nadu, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2305093

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2305093

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition