目的:使用12导联可穿戴式心电图设备,开发一种用于房室结折返性心动过速(AVNRT)和房室折返性心动过速(AVRT)鉴别诊断的智能模型。
方法:将可穿戴设备记录的356份12导联室上性心动过速(SVT)心电图随机分为训练集和验证集,采用5倍交叉验证建立智能分类模型,和101名诊断为SVT的患者从10月开始接受电生理研究和射频消融,2021年3月,2023年被选为测试集。比较诱发性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,构建并验证了用于对SVT机制进行分类的智能诊断模型.来自Ⅱ的3导联心电图信号,III,提取Ⅴ1,建立新的分类模型,将其诊断效能与12导联模型进行比较。
结果:在测试集中的101例SVT患者中,通过电生理研究,68例诊断为AVNRT,33例诊断为AVRT。预训练模型在精确召回曲线(0.9492)和F1得分(0.8195)下实现了高面积,用于识别验证集中的AVNRT。领先的F1总分Ⅱ,III,测试集中的Ⅴ1、3导联和12导联智能诊断模型分别为0.5597、0.6061、0.3419、0.6003和0.6136。与12导联分类模型相比,Lead-Ⅲ模型的净再分类指数改善为-0.029(P=0.878),综合判别指数改善为-0.005(P=0.965)。
结论:使用可穿戴心电图设备的基于多尺度深度神经网络的智能诊断模型对于对SVT机制进行分类具有可接受的准确性。
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).
CONCLUSIONS: The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.