Classifying Abnormalities in Heartbeat Sound

Year : 2024 | Volume :12 | Issue : 01 | Page : 24-31
By

Befina Y.

A. Chandrasekar

  1. Student Computer Science and Engineering, St.Joseph’s College of Engineering, Chennai Tamil Nadu India
  2. Professor Computer Science and Engineering, St.Joseph’s College of Engineering, Chennai Tamil Nadu India

Abstract

Heartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure in their early stages. In the proposed method, various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (mel-frequency cepstral coefficients). Then, a deep learning approach is applied in which the MFCC audio signals are sent to the CNN (convolutional neural network) + LSTM (long short-term memory) model to extract and classify the abnormal heartbeat sounds with its associated disease. Thus, a system for classifying abnormal heartbeat sounds is proposed by leading a way to identify and diagnose diseases that are identified using heartbeat sounds in an early stage.

Keywords: Heartbeat audio signals, deep learning, mel-frequency cepstral coefficients, convolutional neural network, long short-term memory

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications(rrjoesa)]

How to cite this article: Befina Y., A. Chandrasekar. Classifying Abnormalities in Heartbeat Sound. Research & Reviews: A Journal of Embedded System & Applications. 2024; 12(01):24-31.
How to cite this URL: Befina Y., A. Chandrasekar. Classifying Abnormalities in Heartbeat Sound. Research & Reviews: A Journal of Embedded System & Applications. 2024; 12(01):24-31. Available from: https://journals.stmjournals.com/rrjoesa/article=2024/view=139060





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Regular Issue Subscription Review Article
Volume 12
Issue 01
Received March 14, 2024
Accepted March 30, 2024
Published April 4, 2024