Abstract
Heartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure. in an early stage. In the proposed method various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (Melfrequency 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.