Detection of Heart Abnormality with Stethoscope Sounds

Abstract

Cardiac rhythm assessment is a critical step in the early diagnosis of cardiac arrhythmia, which has been identified as a kind of cardiovascular disease (CVD) that affects millions of individuals worldwide. Although electrocardiography is the right test to confirm cardiovascular diseases, due to the time and cost involved in the test, an alternate solution like heart abnormality detection using a stethoscope test is needed. A stethoscope test is a facility that is relatively easily available in rural parts of the country and can aid in the early diagnosis of heart abnormalities. The central aim of this work is to build a deep learning model for classifying heartbeat sounds which are captured with the help of iStethoscope Pro iPhone app or using a digital stethoscope. The proposed methodology is implemented using the Heart Sounds Classification Challenge dataset from PASCAL and the 2016 Physionet Challenge dataset. To extract features from the recorded heart sounds, we employ Mel spectrograms, Mel-frequency cepstral coefficients (MFCC), and Chroma short-time Fourier transform (STFT). A key novelty of our approach lies in the use of the stacked Bidirectional and Unidirectional Long Short Term Memory (SBU-LSTM) and deep BiLSTM architectures, which, combined with the three feature types (Mel spectrograms, Chroma STFT, and MFCC), enhances model performance for the classification task. Additionally, we introduce the use of Mel spectrograms and Chroma STFT with the 2D Convolutional Neural Network (CNN) architecture, which, as far as we know, has not been investigated in prior research. Experimental results show that the best accuracy achieved is 72% for PASCAL’s Dataset-A, 66% for Dataset-B, 83% for Dataset A + B, and 89% for Physionet’s Dataset-C. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.