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.

中文翻译：

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跟随儿童心脏的声音：基于深度学习的计算机辅助小儿冠心病诊断系统

心音听诊是先天性心脏病（CHD）诊断的一种非侵入性且成本较低的方法，尤其是对于儿科患者。近年来，基于深度学习的计算机辅助心音分析得到了广泛的研究和开发。在本文中，我们使用两个新颖的轻量级卷积神经网络（CNN）开发了一种基于深度学习的计算机辅助系统，用于小儿CHD诊断。现有的大多数基于深度学习的系统的关键问题之一是缺乏用于CNN学习的大规模数据集。为此，我们收集了带有医生注释的新生儿和儿童的心音，以构建包含137位受试者的528份高质量录音（总共近4小时）的儿科心音数据集。利用构建的数据集，在计算机辅助的CHD诊断系统中，深层CNN模型可以轻松地作为分类器进行训练。实验结果证明了我们提出的方法在物联网应用中的诊断性能和参数消耗方面的优越性。