Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings

Abstract

Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classification of heart sound recordings but rely on expert features that have not been thoroughly evaluated on noisy recordings. Here we propose a segmental convolutional neural network architecture that achieves automatic feature learning from noisy heart sound recordings. Our experiments show that our best model, trained on noisy recording segments acquired with an existing hidden semi-markov model-based approach, attains a classification accuracy of 87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6% accuracy of the state-of-the-art statistical classifier trained and evaluated on the same dataset. Our results indicate the potential of using neural network-based methods to increase the accuracy of automated classification of heart sound recordings for improved screening of heart diseases.

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

“…Tschannen et al [11] combined a wavelet-based deep CNN feature extractor with support vector machine (SVM) for heart-sound classification. Zhang et al [12] proposed a segmental CNN model to detect cardiac abnormality with two different designs to adjust the configuration of convolutional layers filters. A DL architecture was implemented on field programmable gate array (FPGA) for real-time heart-sound classification using inputs based on gray sonogram images transformed from PCG segments [13].…”