Multi-classification neural network model for detection of abnormal heartbeat audio signals

Abstract

Nowadays, heart disease is the leading cause of death. The high mortality rate and escalating occurrence of heart diseases worldwide warrant the requirement for a fast and efficient diagnosis of such ailments. The purpose is to design an automated system for the classification of abnormal heartbeat audio signals to assist cardiologists. To the best of our knowledge, this is the first study that uses a single neural network model for the classification of eight different types of heartbeat audio signals. The proposed recurrent neural network (RNN) model using Long short-term memory (LSTM) is developed on two publically available databases such as the PASCAL challenge and the 2017 PhysioNet challenge. Mel frequency cepstrum coefficient (MFCC) is applied to extract the dominant features, and a bandpass filter is used to remove the noise from both of the datasets. Afterward, the downsampling technique is used to fix the size of the sampling rate of each sound signal to 20KHz and 300 Hz for the Pascal and PhysioNet database, respectively. The proposed model is compared with multi-layer perceptron (MLP) in terms of different performance evaluation matrices. Furthermore, the outcomes of five machine learning (ML) models are also analyzed. The proposed model has achieved the highest classification accuracy of 0.9971 on the Pascal database, and 0.9870 accuracy on the PhysioNet challenge dataset, which is consistently superior to its competitor approaches. The proposed model provides significant assistance to the cardiac consultant in detecting heart valve diseases.