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Follow the Sound of Children’s Heart: A Deep-Learning-Based Computer-Aided Pediatric CHDs Diagnosis System
IEEE Internet of Things Journal ( IF 9.515 ) Pub Date : 2019-12-20 , DOI: 10.1109/jiot.2019.2961132
Bin Xiao; Yunqiu Xu; Xiuli Bi; Weisheng Li; Zhuo Ma; Junhui Zhang; Xu Ma

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.
更新日期:2020-03-16

 

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