Computer Science Department, Technion-IIT, Haifa, Israel.
Laboratory of Bioenergetic and Bioelectric Systems, Biomedical Engineering Faculty, Technion-IIT, Haifa, Israel.
Sci Rep. 2020 Oct 1;10(1):16331. doi: 10.1038/s41598-020-73060-w.
Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to diagnose changes in heart function. The value of automated 12-lead ECG diagnostic approaches lies in their ability to screen the general population and to provide a second opinion for doctors. Yet, the clinical utility of automated ECG interpretations remains limited. We introduce a two-way approach to an automated cardiac disease identification system using standard digital or image 12-lead ECG recordings. Two different network architectures, one trained using digital signals (CNN-dig) and one trained using images (CNN-ima), were generated. An open-source dataset of 41,830 classified standard ECG recordings from patients and volunteers was generated. CNN-ima was trained to identify atrial fibrillation (AF) using 12-lead ECG digital signals and images that were also transformed to mimic mobile device camera-acquired ECG plot snapshots. CNN-dig accurately (92.9-100%) identified every possible combination of the eight most-common cardiac conditions. Both CNN-dig and CNN-ima accurately (98%) detected AF from standard 12-lead ECG digital signals and images, respectively. Similar classification accuracy was achieved with images containing smartphone camera acquisition artifacts. Automated detection of cardiac conditions in standard digital or image 12-lead ECG signals is feasible and may improve current diagnostic methods.
标准 12 导联心电图（ECG）被用作诊断心脏功能变化的主要临床工具。自动化 12 导联 ECG 诊断方法的价值在于其能够对普通人群进行筛查，并为医生提供第二意见。然而，自动化 ECG 解释的临床实用性仍然有限。我们提出了一种使用标准数字或图像 12 导联 ECG 记录的自动化心脏疾病识别系统的双向方法。生成了两种不同的网络架构，一种使用数字信号训练（CNN-dig），另一种使用图像训练（CNN-ima）。从患者和志愿者中生成了一个包含 41830 个分类标准 ECG 记录的开源数据集。CNN-ima 使用 12 导联 ECG 数字信号和图像进行房颤（AF）的识别训练，这些图像也被转换为模拟移动设备相机采集的 ECG 图快照。CNN-dig 能够准确（92.9-100%）识别出八种最常见心脏疾病的所有可能组合。CNN-dig 和 CNN-ima 都能准确（98%）地从标准 12 导联 ECG 数字信号和图像中检测到 AF。包含智能手机相机采集伪影的图像也能达到类似的分类准确性。在标准数字或图像 12 导联 ECG 信号中自动检测心脏疾病是可行的，可能会改进当前的诊断方法。
