Wang H, Mi L, Zhang Y, Ge L, Lai J, Chen T, Li J, Shi X, Xiu J, Tang M, Yang W, Guo J
Department of Cardiovascular Medicine, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China.
Arrhythmia Center of Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 May 20;44(5):851-858. doi: 10.12122/j.issn.1673-4254.2024.05.06.
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.
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 Ⅴ were extracted to build new classification models, whose diagnostic efficacy was compared with that of the 12-lead model.
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, Ⅴ, 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 (=0.878) and an integrated discrimination index improvement of -0.005 (=0.965).
The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.
利用12导联可穿戴式心电图设备开发一种用于鉴别诊断房室结折返性心动过速(AVNRT)和房室折返性心动过速(AVRT)的智能模型。
将可穿戴设备记录的356份12导联室上性心动过速(SVT)心电图样本采用5折交叉验证法随机分为训练集和验证集,以建立智能分类模型,并选取2021年10月至2023年3月期间101例诊断为SVT并接受电生理检查和射频消融的患者作为测试集。比较诱发心动过速前后心电图参数的变化。基于多尺度深度神经网络,构建并验证了一种用于分类SVT机制的智能诊断模型。提取Ⅱ、Ⅲ、Ⅴ导联的3导联心电图信号以建立新的分类模型,并将其诊断效能与12导联模型进行比较。
测试集中101例SVT患者中,经电生理检查诊断为AVNRT的有68例,诊断为AVRT的有33例。预训练模型在验证集中识别AVNRT时,精确召回曲线下面积(0.9492)和F1分数(0.8195)较高。测试集中Ⅱ、Ⅲ、Ⅴ导联、3导联和12导联智能诊断模型的总F1分数分别为0.5597、0.6061、0.3419、0.6003和0.6136。与12导联分类模型相比,Ⅲ导联模型的净重新分类指数改善为-0.029(=0.878),综合判别指数改善为-0.005(=0.965)。
基于多尺度深度神经网络的可穿戴式心电图设备智能诊断模型在分类SVT机制方面具有可接受的准确性。