Hata Erika, Seo Chanjin, Nakayama Masafumi, Iwasaki Kiyotaka, Ohkawauchi Takaaki, Ohya Jun
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1548-1551. doi: 10.1109/EMBC44109.2020.9175151.
This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study, one-beat ECG images for 12-leads and 4-leads are generated from ECG's and train CNN's (Convolutional neural network). By applying the Grad-CAM to the trained CNN's, feature areas are detected in the early time range of the one-beat ECG image. Also, by limiting the time range of the ECG image to that of the feature area, the CNN for the 4-lead achieves the best classification performance, which is close to expert medical doctors' diagnoses.Clinical Relevance-This paper achieves as high AS classification performance as medical doctors' diagnoses based on echocardiograms by proposing an automatic method for detecting AS only using ECG.
本文提出了一种利用深度学习通过心电图（ECG）图像对主动脉瓣狭窄（AS）进行分类的自动方法，其训练心电图图像由观察超声心动图的医生给出的诊断进行标注。此外，还利用梯度加权类激活映射（Grad-CAM）探索了训练后的深度学习网络与其诊断结果之间的关系。在本研究中，从心电图生成12导联和4导联的单搏心电图图像，并训练卷积神经网络（CNN）。通过将Grad-CAM应用于训练后的CNN，在单搏心电图图像的早期时间范围内检测到特征区域。此外，通过将心电图图像的时间范围限制为特征区域的时间范围，4导联的CNN实现了最佳分类性能，接近医学专家的诊断。临床相关性——本文通过提出一种仅使用心电图检测AS的自动方法，实现了与基于超声心动图的医生诊断一样高的AS分类性能。
