Asmare Melkamu Hunegnaw, Woldehanna Frehiwot, Janssens Luc, Vanrumste Bart
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:168-171. doi: 10.1109/EMBC44109.2020.9176544.
Rheumatic Heart Disease (RHD) is an autoimmune response to a bacterial attack which deteriorates the normal functioning of the heart valves. The damage on the valves affects the normal blood flow inside the heart chambers which can be recorded and listened to via a stethoscope as a phonocardiogram. However, the manual method of auscultation is difficult, time consuming and subjective. In this study, a convolutional neural network based deep learning algorithm is used to perform an automatic auscultation and it classifies the heart sound as normal and rheumatic. The classification is done on un-segmented data where the extraction of the first, the second and systolic and diastolic heart sounds are not required. The architecture of the CNN network is formed as an array of layers. Convolutional and batch normalization layers followed by a max pooling layer to down sample the feature maps are used. At the end there is a final max pooling layer which pools the input feature map globally over time and at the end a fully connected layer is included. The network has five convolutional layers. This current work illustrates the use of deep convolutional neural network using a Mel Spectro-temporal representation. For this current study, an RHD heart sound data set is recorded from one hundred seventy subjects from whom one hundred twenty four are confirmed RHD patients. The system has an overall accuracy of 96.1% with 94.0% sensitivity and 98.1% and specificity.
风湿性心脏病（RHD）是对细菌攻击的一种自身免疫反应，会使心脏瓣膜的正常功能恶化。瓣膜上的损伤会影响心腔内的正常血流，通过听诊器可以记录并听到这种血流声，即心音图。然而，人工听诊方法困难、耗时且主观。在本研究中，使用基于卷积神经网络的深度学习算法进行自动听诊，并将心音分类为正常和风湿性。分类是在未分割的数据上进行的，不需要提取第一、第二心音以及收缩期和舒张期心音。CNN网络的架构由一系列层组成。使用卷积层和批归一化层，随后是一个最大池化层来对特征图进行下采样。最后有一个最终的最大池化层，它在时间上全局池化输入特征图，最后包含一个全连接层。该网络有五个卷积层。当前这项工作展示了使用梅尔频谱 - 时间表示的深度卷积神经网络。对于当前这项研究，从170名受试者中记录了RHD心音数据集，其中124名是确诊的RHD患者。该系统的总体准确率为96.1%，灵敏度为94.0%，特异性为98.1%。
