Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
Department of Cardiovascular Surgery, Gansu Provincial Hospital, Lanzhou, Gansu 730000, China.
J Healthc Eng. 2020 May 9;2020:9640821. doi: 10.1155/2020/9640821. eCollection 2020.
Heart auscultation is a convenient tool for early diagnosis of heart diseases and is being developed to be an intelligent tool used in online medicine. Currently, there are few studies on intelligent diagnosis of pediatric murmurs due to congenital heart disease (CHD). The purpose of the study was to develop a method of intelligent diagnosis of pediatric CHD murmurs. Phonocardiogram (PCG) signals of 86 children were recorded with 24 children having normal heart sounds and 62 children having CHD murmurs. A segmentation method based on the discrete wavelet transform combined with Hadamard product was implemented to locate the first and the second heart sounds from the PCG signal. Ten features specific to CHD murmurs were extracted as the input of classifier after segmentation. Eighty-six artificial neural network classifiers were composed into a classification system to identify CHD murmurs. The accuracy, sensitivity, and specificity of diagnosis for heart murmurs were 93%, 93.5%, and 91.7%, respectively. In conclusion, a method of intelligent diagnosis of pediatric CHD murmurs is developed successfully and can be used for online screening of CHD in children.
心脏听诊是早期诊断心脏病的便捷工具，目前正在开发成为在线医疗中使用的智能工具。由于先天性心脏病 (CHD)，目前针对儿科心杂音的智能诊断研究较少。本研究旨在开发一种儿科 CHD 心杂音的智能诊断方法。使用 24 名心音正常的儿童和 62 名患有 CHD 心杂音的儿童记录心音图 (PCG) 信号。实施了一种基于离散小波变换和 Hadamard 乘积的分段方法，从 PCG 信号中定位第一和第二心音。分段后，提取了 10 个特定于 CHD 心杂音的特征作为分类器的输入。将 86 个人工神经网络分类器组成一个分类系统来识别 CHD 心杂音。心杂音的诊断准确率、灵敏度和特异性分别为 93%、93.5%和 91.7%。总之，成功开发了一种儿科 CHD 心杂音的智能诊断方法，可用于儿童 CHD 的在线筛查。
