Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence
نویسندگان
چکیده
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, deep learning (DL)-based handheld device was deployed to facilitate symptom-driven monitoring. Methods: A cohort patients with paroxysmal (AF) trained use DL-based record ECG signals presented after ablation. Additionally, 24-hour Holter monitoring and 12-lead were scheduled at 3, 6, 9, 12 months post-ablation. The AFR by different modalities explored. Results: total 22 67 experienced AFR. monitor detected 19 8 events, respectively, five which identified both modalities. larger portion tracings recorded for than without [362(330) vs. 132(133), P=0.01)], substantial numbers events from 18:00 24:00. Compared Holter, more earlier stages (HR=1.6, 95% CI 1.2–2.2, P<0.01). Conclusions: device-enabled recording, compared conventional strategy, improved enabled identification symptomatic episodes.
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ژورنال
عنوان ژورنال: Cardiovascular innovations and applications
سال: 2023
ISSN: ['2009-8782', '2009-8618']
DOI: https://doi.org/10.15212/cvia.2023.0048