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Cardiovascular diseases are one of the leading cause of death in today'sworld and early screening of heart condition plays a crucial role in preventingthem. The heart sound signal is one of the primary indicator of heart conditionand can be used to detect abnormality in the heart. The acquisition of heartsound signal is non-invasive, cost effective and requires minimum equipment.But currently the detection of heart abnormality from heart sound signaldepends largely on the expertise and experience of the physician. As such anautomatic detection system for heart abnormality detection from heart soundsignal can be a great asset for the people living in underdeveloped areas. Inthis paper we propose a novel deep learning based dual stream network withattention mechanism that uses both the raw heart sound signal and the MFCCfeatures to detect abnormality in heart condition of a patient. The deep neuralnetwork has a convolutional stream that uses the raw heart sound signal and arecurrent stream that uses the MFCC features of the signal. The features fromthese two streams are merged together using a novel attention network andpassed through the classification network. The model is trained on the largestpublicly 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.

Heart Abnormality Detection from Heart Sound Signals using MFCC Feature and Dual Stream Attention Based Network