Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence  

作　　者：Laite Chen Chenyang Jiang 

机构地区：[1]Department of Cardiology of Sir Run Run Shaw Hospital,School of Medicine,Zhejiang University,China

出　　处：《Cardiovascular Innovations and Applications》2023年第1期405-412,共8页心血管创新与应用(英文)

摘　　要：Objective:Symptom-driven electrocardiogram(ECG)recording plays a significant role in the detection of post-ablation atrial fibrillation recurrence(AFR).However,making timely medical contact whenever symptoms occur may not be practical.Herein,a deep learning(DL)-based handheld device was deployed to facilitate symptom-driven monitoring.Methods:A cohort of patients with paroxysmal atrial fibrillation(AF)was trained to use a DL-based handheld device to record ECG signals whenever symptoms presented after the ablation.Additionally,24-hour Holter monitoring and 12-lead ECG were scheduled at 3,6,9,and 12 months post-ablation.The detection of AFR by the different modalities was explored.Results:A total of 22 of 67 patients experienced AFR.The handheld device and 24-hour Holter monitor detected 19 and 8 AFR events,respectively,five of which were identified by both modalities.A larger portion of ECG tracings was recorded for patients with than without AFR[362(330)vs.132(133),P=0.01],and substantial numbers of AFR events were recorded from 18:00 to 24:00.Compared to Holter,moreAFR events were detected by the handheld device in earlier stages(HR=1.6,95%CI 1.2–2.2,P<0.01).Conclusions:The DL-based handheld device-enabled symptom-driven recording,compared with the conventional monitoring strategy,improved AFR detection and enabled more timely identification of symptomatic episodes.

关 键 词：Symptom-driven recorder 24-hour Holter Atrial fibrillation radiofrequency catheter ablation Follow-up RECURRENCE 

分 类 号：R541.7[医药卫生—心血管疾病]