Visagie C, Scheffer C, Lubbe W W, Doubell A F
Department of Mechanical and Mechatronic Engineering, Stellenbosch University, Matieland 7602, South Africa.
Australas Phys Eng Sci Med. 2009 Dec;32(4):240-50. doi: 10.1007/BF03179245.
This paper presents a study using an auscultation jacket with embedded electronic stethoscopes, and a software classification system capable of differentiating between normal and certain auscultatory abnormalities. The aim of the study is to demonstrate the potential of such a system for semi-automated diagnosis for underserved locations, for instance in rural areas or in developing countries where patients far outnumber the available medical personnel. Using an "auscultation jacket", synchronous data was recorded at multiple chest locations on 31 healthy volunteers and 21 patients with heart pathologies. Electrocardiograms (ECGs) were also recorded simultaneously with phonocardiographic data. Features related to heart pathologies were extracted from the signals and used as input to a feed-forward artificial neural network. The system is able to classify between normal and certain abnormal heart sounds with a sensitivity of 84% and a specificity of 86%. Though the number of training and testing samples presented are limited, the system performed well in differentiating between normal and abnormal heart sounds in the given database of available recordings. The results of this study demonstrate the potential of such a system to be used as a fast and cost-effective screening tool for heart pathologies.
本文介绍了一项研究，该研究使用了带有嵌入式电子听诊器的听诊夹克以及一个能够区分正常听诊和某些听诊异常的软件分类系统。该研究的目的是证明这种系统在医疗资源匮乏地区（例如农村地区或发展中国家，那里患者数量远远超过现有医务人员数量）进行半自动诊断的潜力。使用“听诊夹克”，在31名健康志愿者和21名患有心脏疾病的患者的多个胸部位置记录了同步数据。心电图（ECG）也与心音图数据同时记录。从信号中提取与心脏疾病相关的特征，并将其用作前馈人工神经网络的输入。该系统能够以84%的灵敏度和86%的特异性对正常和某些异常心音进行分类。尽管所呈现的训练和测试样本数量有限，但该系统在给定的可用录音数据库中区分正常和异常心音方面表现良好。这项研究的结果证明了这种系统作为一种快速且经济高效的心脏疾病筛查工具的潜力。
