Heart sound classification using deep structured features

Abstract

We present a novel machine learning-based method for heart sound classification which we submitted to the PhysioNet/CinC Challenge 2016. Our method relies on a robust feature representation - generated by a wavelet-based deep convolutional neural network (CNN) - of each cardiac cycle in the test recording, and support vector machine classification. In addition to the CNN-based features, our method incorporates physiological and spectral features to summarize the characteristics of the entire test recording. The proposed method obtained a score, sensitivity, and specificity of 0.812, 0.848, and 0.776, respectively, on the hidden challenge testing set.