Heart Abnormality Detection from Heart Sound Signals using MFCC Feature and Dual Stream Attention Based Network
This work addresses the need for an automatic detection system to assist in early screening of cardiovascular diseases, particularly benefiting people in underdeveloped areas by reducing reliance on expert physicians.
The paper tackles the problem of automatically detecting heart abnormalities from heart sound signals by proposing a novel deep learning-based dual stream network with attention mechanism, achieving an accuracy of 87.11% and sensitivity of 82.41% on the largest publicly available PCG dataset.
Cardiovascular diseases are one of the leading cause of death in today's world and early screening of heart condition plays a crucial role in preventing them. The heart sound signal is one of the primary indicator of heart condition and can be used to detect abnormality in the heart. The acquisition of heart sound signal is non-invasive, cost effective and requires minimum equipment. But currently the detection of heart abnormality from heart sound signal depends largely on the expertise and experience of the physician. As such an automatic detection system for heart abnormality detection from heart sound signal can be a great asset for the people living in underdeveloped areas. In this paper we propose a novel deep learning based dual stream network with attention mechanism that uses both the raw heart sound signal and the MFCC features to detect abnormality in heart condition of a patient. The deep neural network has a convolutional stream that uses the raw heart sound signal and a recurrent stream that uses the MFCC features of the signal. The features from these two streams are merged together using a novel attention network and passed through the classification network. The model is trained on the largest publicly available dataset of PCG signal and achieves an accuracy of 87.11, sensitivity of 82.41, specificty of 91.8 and a MACC of 87.12.