Automatic Classification of Heart Sounds Using Long Short-Term Memory

• Published in: International Conference on Advanced Communication Control and Computing Technologies (Online) pp. 1 - 6
• Main Authors: Ahmad, Bilal, Khan, Faiq Ahmad, Khan, Kaleem Nawaz, Khan, Muhammad Salman
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.12.2021
• Subjects: discrete wavelet transform (DWT); Discrete wavelet transforms; Heart; long short-term memory (LSTM); machine learning; Machine learning algorithms; Mel-frequency cepstral coefficients (MFCC); Phonocardiogram (PCG); Sensitivity; Sensitivity and specificity; Training; Wavelet domain
• ISSN: 2644-206X
• DOI: 10.1109/ICOSST53930.2021.9683975

Heart diseases are serious and must be detected early using an auscultation examination. To explore and diagnose heart problems, several signal processing and machine learning approaches are used. From a Phonocardiogram (PCG) signal, the heart sound (HS) can be categorized into normal and abnormal. This paper presents an improvedcomputer-aidedtechniquefor classification of HS using long short-term memory (LSTM)deployed withdifferent time and frequency domain features, i.e., discrete wavelet transform (DWT) and Mel-frequency cepstral coefficients (MFCCs). The overall score, accuracy, sensitivity, and specificity of the LSTM classifier are calculated for the performance evaluation. With the proposed set of experimentsthe classification algorithm achieved a final score of 90.04% (Accuracy 90%, Sensitivity 92.30%, and Specificity 87.69%).