IEEE J Biomed Health Inform. 2020 Jun;24(6):1569-1578. doi: 10.1109/JBHI.2020.2980454. Epub 2020 Mar 13.
This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibrillation (AF) with special emphasis on the combination of deep learning based algorithm and modified patch-based ECG lead. Fifty-five consecutive patients were monitored for AF in around 24 hours by patch-based ECG devices along with a standard 12-lead Holter. Catering to potential positional variability of patch lead, four typical positions on the upper-left chest were proposed. For each patch lead, the performance of automated algorithms with four different convolutional neural networks (CNN) was evaluated for AF detection against blinded annotations of two clinicians. A total of 349,388 10-second segments of AF and 161,084 segments of sinus rhythm were detected successfully. Good agreement between patch-based single-lead and standard 12-lead recordings was obtained at the position MP1 that corresponds to modified lead II, and a promising performance of the automated algorithm with an R-R intervals based CNN model was achieved on this lead in terms of accuracy (93.1%), sensitivity (93.1%), and specificity (93.4%). The present results suggest that the optimized patch-based ECG lead along by deep learning based algorithms may offer the possibility of providing an accurate, easy, and inexpensive clinical tool for mass screening of AF.
本研究旨在评估使用非标准化单导联心电图（ECG）监测自动检测心房颤动（AF）的可行性，特别强调基于深度学习的算法和改良贴片式 ECG 导联的结合。对 55 例连续患者进行了约 24 小时的 AF 监测，采用贴片式 ECG 设备和标准的 12 导联 Holter。针对贴片导联潜在的位置变化，提出了左上胸部的四个典型位置。对于每个贴片导联，使用四个不同的卷积神经网络（CNN）对自动算法进行评估，以检测 AF 并与两名临床医生的盲注释进行比较。成功检测到 349,388 个 10 秒的 AF 段和 161,084 个窦律段。在对应改良导联 II 的位置 MP1 上，贴片式单导联和标准 12 导联记录之间获得了良好的一致性，并且基于 RR 间隔的 CNN 模型的自动算法在该导联上具有较高的准确性（93.1%）、敏感性（93.1%）和特异性（93.4%）。这些结果表明，经过优化的贴片式 ECG 导联与基于深度学习的算法相结合，可能为 AF 的大规模筛查提供一种准确、简便、廉价的临床工具。
