Follow the Sound of Children’s Heart: A Deep-Learning-Based Computer-Aided Pediatric CHDs Diagnosis System

作者：Bin Xiao, Yunqiu Xu, Xiuli Bi, Weisheng Li, Zhuo Ma, Junhui Zhang, Xu Ma · 发表于：IEEE Internet of Things Journal · 年份：2019 · DOI：10.1109/jiot.2019.2961132 · 被引用次数：86 · 研究领域：Phonocardiography and Auscultation Techniques、Music and Audio Processing

Auscultation of heart sounds is a noninvasive and less costly way for congenital heart disease (CHD) diagnosis, especially for pediatric individuals. The deep-learning-based computer-aided heart sound analysis has been widely studied and developed in recent years. In this article, we develop a deep-learning-based computer-aided system for pediatric CHDs diagnosis using two novel lightweight convolution neural networks (CNNs). One key issue of most existing deep-learning-based systems is the scarcity of large-scale data sets for CNN learning. To this end, we collect heart sounds from newborns and children with physicians' annotations to construct a pediatric heart sound data set that contains 528 high-quality recordings (nearly 4 h in total) from 137 subjects. With the constructed data set, deep CNN models can be easily trained as classifiers in computer-aided CHDs diagnosis systems. The experimental results demonstrate the superiority of our proposed methods in terms of diagnosis performance and parameter consumption in the application of Internet of Things.