Department of Cardiology, Chongqing Kanghua Zhonglian Cardiovascular Hospital, 163 Haier Road, Jiangbei District, Chongqing City 400000, China.
Comput Math Methods Med. 2022 Mar 23;2022:6491084. doi: 10.1155/2022/6491084. eCollection 2022.
This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) in the efficacy evaluation of radiofrequency ablation in the treatment of tachyarrhythmia. In this study, 158 patients with rapid arrhythmia treated by radiofrequency ablation were divided into effective treatment group (142 cases) and ineffective treatment group (16 cases). ECG examination was performed on all patients, and the indicators of ECG examination were quantified by the deep learning-based convolutional neural network model. The indicators of ECG examination of the effective treatment group and the ineffective treatment group were compared. The results showed that compared with the ineffective treatment group, the end-systolic volume (ESV), end-diastolic volume (EDV), end-systolic volume index (ESVI), and end-diastolic volume index (EDVI) of the effective treatment group were significantly decreased, and the left ventricular ejection fraction (LVEF) was significantly increased ( < 0.05). After radiofrequency ablation, the ventricular rate of patients in the effective treatment group was significantly lower than that of the ineffective treatment group at 12 h and 24 h after treatment ( < 0.05). In addition, compared with patients in the ineffective treatment group, the QT dispersion of the ECG in the effective treatment group was significantly higher ( < 0.05). The accuracy, specificity, and sensitivity of ECG in evaluating the therapeutic effect of patients with tachyarrhythmia were 86.81%, 84.29%, and 77.27%, respectively. The area under the curve was determined as 0.798 according to the receiver operating characteristic (ROC) curve of the subjects. In summary, indicators of ECG examination based on deep learning can provide auxiliary reference information for the efficacy evaluation of radiofrequency ablation in the treatment of tachyarrhythmia.
本研究旨在分析基于深度学习的心电图（ECG）在评估射频消融治疗快速性心律失常疗效中的重要作用。本研究纳入 158 例接受射频消融治疗的快速性心律失常患者，分为有效治疗组（142 例）和无效治疗组（16 例）。所有患者均行 ECG 检查，并通过基于深度学习的卷积神经网络模型对 ECG 检查指标进行量化。比较有效治疗组和无效治疗组的 ECG 检查指标。结果显示，与无效治疗组相比，有效治疗组的收缩末期容积（ESV）、舒张末期容积（EDV）、收缩末期容积指数（ESVI）和舒张末期容积指数（EDVI）明显降低，左心室射血分数（LVEF）明显升高（<0.05）。射频消融后，有效治疗组患者的心室率在治疗后 12 h 和 24 h 明显低于无效治疗组（<0.05）。此外，与无效治疗组患者相比，有效治疗组的心电图 QT 离散度明显更高（<0.05）。心电图评估快速性心律失常患者治疗效果的准确率、特异度和敏感度分别为 86.81%、84.29%和 77.27%，受试者工作特征（ROC）曲线下面积为 0.798。综上所述，基于深度学习的 ECG 检查指标可为射频消融治疗快速性心律失常的疗效评估提供辅助参考信息。
