Sau Arunashis, Ibrahim Safi, Ahmed Amar, Handa Balvinder, Kramer Daniel B, Waks Jonathan W, Arnold Ahran D, Howard James P, Qureshi Norman, Koa-Wing Michael, Keene Daniel, Malcolme-Lawes Louisa, Lefroy David C, Linton Nicholas W F, Lim Phang Boon, Varnava Amanda, Whinnett Zachary I, Kanagaratnam Prapa, Mandic Danilo, Peters Nicholas S, Ng Fu Siong
National Heart and Lung Institute, Imperial College London, Du Cane Road, London W12 0NN, UK.
Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, Du Cane Road, London W12 0NN, UK.
Eur Heart J Digit Health. 2022 Aug 17;3(3):405-414. doi: 10.1093/ehjdh/ztac042. eCollection 2022 Sep.
Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.
We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77-0.95) compared to median expert electrophysiologist accuracy of 79% (range 70-84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.
We describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.
从12导联心电图（ECG）准确判断房性心律失常机制具有挑战性。鉴于三尖瓣峡部（CTI）消融成功率高，识别CTI依赖性典型房扑（AFL）对于治疗决策和手术规划很重要。我们试图训练一个卷积神经网络（CNN），以侵入性电生理（EP）研究数据作为金标准，对CTI依赖性AFL与非CTI依赖性房性心动过速（AT）进行分类。
我们使用231例接受房性快速性心律失常EP研究患者的数据训练CNN。总共13500个五秒的12导联ECG片段用于训练。根据EP研究结果，每个病例被标记为CTI依赖性AFL或非CTI依赖性AT。针对57例患者的测试集评估模型性能。对欧洲的电生理学家就相同的57份ECG进行了调查。该模型的准确率为86%（95%CI 0.77 - 0.95），而电生理专家的中位数准确率为79%（范围70 - 84%）。在测试集病例的三分之二（38/57）中，模型和电生理学家的共识一致，预测准确率为100%。显著性映射表明心房激活是ECG中决定模型输出的最重要部分。
我们描述了首个使用ECG训练的用于区分CTI依赖性AFL与其他AT的CNN。我们的模型与电生理专家的表现相当且互为补充。自动化人工智能增强的ECG分析有助于指导有组织性房性心律失常患者的治疗决策和规划消融手术。
