The Implementation of Pretrained AlexNet on PCG Classification

Abstract

Heart sounds are essential components of cardiac diagnosis, in which heart conditions can be detected using phonocardiogram (PCG) signals. PCG signals provide useful information and can help in the early detection and diagnosis of heart diseases. Many studies have attempted to discover automated tools that analyse heart sounds by applying machine learning algorithms. Although tremendous efforts have been made in this area, no successful framework currently exists to detect pathology in signals because of issues with background noise or poor quality. One part of the evolution of machine learning is the development of deep learning networks, which are designed to exploit the compositional structure of data. This paper investigates the performance of a convolutional neural network called AlexNet, focusing on two approaches for distinguishing abnormality in PCG signals with data collected from the 2016 PhysioNet/CinC Challenge dataset. This dataset contains heart sound recordings collected from clinical and non-clinical environments. Our extensive simulation results indicated that using AlexNet as feature extractor and Support Vector Machine as classifier a 87% recognition accuracy was achieved, this is an improvement of 85% accuracy obtained by end-to-end learning AlexNet in comparison to the benchmarked techniques.