Explainable Deep Convolutional Neural Network for Valvular Heart Diseases Classification Using PCG Signals

Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : The heart sounds, recorded through a phonocardiogram (PCG) can assist in the early detection of valvular heart diseases (VHD), which is important for minimizing the chances of cardiac complications like heart failure and sudden cardiac death. Despite a large number of previous studies with highly accurate classifications, PCG-based deep learning (DL) methods for VHD detection are not suitable for clinical practice as they lack transparency and interpretability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> The proposed work utilizes analytic continuous wavelet transform (CWT) scalograms as the time-frequency representations (TFR) of the PCG signals. A 2D convolutional neural network (CNN) is designed for the multiclass classification (Aortic stenosis, Mitral regurgitation, Mitral stenosis, Mitral valve prolapse, Normal) of PCG signal’s TFR. Besides introducing a VHD classification method in this paper, we also carry out the interpretation of the proposed CNN architecture by using occlusion maps and deep dream images for local and global explanations respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The highest accuracy achieved during fivefold cross validation is 99.6% and the overall accuracy is 98.32% for a publicly available PCG database. The accuracy of the proposed method for binary classification (Abnormal, Normal), tested on the PhysioNet database is 93.07%. DL visualization methods assisted in determining what features or regions of TFR of PCGs the proposed network was “looking at” for making a class-specific correct prediction. Class-specific morphological and timefrequency features were observed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> DL visualization makes the network decision more reliable. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> The novel classification framework along with its interpretations would enable successful clinical translation of PCG-based VHD detection.

Topics & Concepts