Objective: To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia (AVNRT) and atrioventricular re-entrant tachycardia (AVRT) using 12-lead wearable electrocardiogram devices.
Methods: A total of 356 samples of 12-lead supraventricular tachycardia (SVT) electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model, and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October, 2021 to March, 2023 were selected as the testing set. The changes in electrocardiogram parameters before and during induced tachycardia were compared. Based on multiscale deep neural network, an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated. The 3-lead electrocardiogram signals from Ⅱ, Ⅲ, and Ⅴ1 were extracted to build new classification models, whose diagnostic efficacy was compared with that of the 12-lead model.
Results: Of the 101 patients with SVT in the testing set, 68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study. The pre-trained model achieved a high area under the precision-recall curve (0.9492) and F1 score (0.8195) for identifying AVNRT in the validation set. The total F1 scores of the lead Ⅱ, Ⅲ, Ⅴ1, 3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597, 0.6061, 0.3419, 0.6003 and 0.6136, respectively. Compared with the 12-lead classification model, the lead-Ⅲ model had a net reclassification index improvement of -0.029 (P=0.878) and an integrated discrimination index improvement of -0.005 (P=0.965).
Conclusion: The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.
目的: 基于十二导联穿戴式心电设备，探索室上性心动过速（SVT）机制鉴别的智能分类模型。
方法: 选取356份SVT的穿戴式心电图，通过五折交叉验证的方式随机分为训练集、验证集建立智能分类模型，选取2021年10月~2023年3月诊断为SVT并行电生理检查及射频消融术的患者共101例作为测试集。对比心动过速诱发前后的心电图参数改变，基于多尺度深度神经网络，并加入窦性心律对比图增强训练，建立SVT机制分类的智能分类模型并验证诊断效能。进一步提取II，III，V1三导联心电信号建立分类模型，并对比其与十二导联智能分类模型的效能。
结果: 101例测试集中68例为房室结折返性心动过速，33例为房室折返性心动过速。预训练模型在验证集中识别房室结折返性心动过速的最高精确率-召回率曲线下面积达到0.9492，F1评分为0.8195。最终II导联，III导联，V1导联，三导联与十二导联智能分类模型于测试集中的总F1评分分别为0.5597，0.6061，0.3419，0.6003与0.6136。对比十二导联，III导联的净重新分类指数与综合判别改善指数分别为-0.029（P=0.714）与-0.005（P=0.817）。
结论: 基于多尺度深度神经网络，初步建立了穿戴式心电图对SVT机制分类的智能分类模型，并具有一定的准确性。
Keywords: 12-lead electrocardiogram; atrioventricular nodal re-entrant tachycardia; atrioventricular re-entrant tachycardia; multiscale deep neural network; supraventricular tachycardia; wearable electrocardiogram.