Arnold Ahran D, Howard James P, Gopi Aiswarya A, Chan Cheng Pou, Ali Nadine, Keene Daniel, Shun-Shin Matthew J, Ahmad Yousif, Wright Ian J, Ng Fu Siong, Linton Nick W F, Kanagaratnam Prapa, Peters Nicholas S, Rueckert Daniel, Francis Darrel P, Whinnett Zachary I
National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom.
Cardiovasc Digit Health J. 2020 Jul-Aug;1(1):11-20. doi: 10.1016/j.cvdhj.2020.07.001.
His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts.
The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation.
We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset.
The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network's performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; <.0001), with an overall accuracy of 75%. The CNN's accuracy in the 17-patient testing set was 67% for S-HBP, 71% for NS-HBP, and 84% for MOC.
We demonstrated proof of concept that a neural network can be trained to automate discrimination between HBP ECG responses. When a larger dataset is trained to higher accuracy, automated AI ECG analysis could facilitate HBP implantation and follow-up and prevent complications resulting from incorrect HBP ECG analysis.
希氏束起搏（HBP）因其能够实现生理性心室激动，已成为传统心室起搏的一种替代方法。在希氏束进行起搏会产生不同的心电图（ECG）反应：选择性希氏束起搏（S-HBP）、非选择性希氏束起搏（NS-HBP）和仅心肌夺获（MOC）。必须将这三种夺获类型相互区分开来，即使对于专家而言，这也可能具有挑战性且耗时。
本研究的目的是使用卷积神经网络（CNN）进行监督式机器学习形式的人工智能（AI），以实现HBP心电图解读的自动化。
我们确定了接受过HBP的患者，并提取了在S-HBP、NS-HBP和MOC期间的原始12导联心电图数据。使用3折交叉验证，在75%标记有夺获类型的分段QRS波群上训练CNN。其余25%留作测试数据集。
CNN使用来自59名患者的1297个QRS波群进行训练。神经网络在17名患者测试集上的性能的Cohen kappa为0.59（95%置信区间0.30至0.88；P<.0001），总体准确率为75%。CNN在17名患者测试集中对S-HBP的准确率为67%，对NS-HBP的准确率为71%，对MOC的准确率为84%。
我们证明了一个概念，即可以训练神经网络来自动区分HBP心电图反应。当使用更大的数据集训练至更高的准确率时，自动化AI心电图分析可以促进HBP植入和随访，并预防因HBP心电图分析错误导致的并发症。
