Introduction: Deep learning models for detecting episodes of atrial
fibrillation (AF) using rhythm information in long-term, ambulatory ECG
recordings have shown high performance. However, the rhythm-based approach does
not take advantage of the morphological information conveyed by the different
ECG waveforms, particularly the f-waves. As a result, the performance of such
models may be inherently limited. Methods: To address this limitation, we have
developed a deep learning model, named RawECGNet, to detect episodes of AF and
atrial flutter (AFl) using the raw, single-lead ECG. We compare the
generalization performance of RawECGNet on two external data sets that account
for distribution shifts in geography, ethnicity, and lead position. RawECGNet
is further benchmarked against a state-of-the-art deep learning model, named
ArNet2, which utilizes rhythm information as input. Results: Using RawECGNet,
the results for the different leads in the external test sets in terms of the
F1 score were 0.91--0.94 in RBDB and 0.93 in SHDB, compared to 0.89--0.91 in
RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a
high-performance, generalizable algorithm for detection of AF and AFl episodes,
exploiting information on both rhythm and morphology.

使用深度学习模型 RawECGNet，通过节律和形态学信息，高效、可泛化地检测房颤和心房扑动的发作。