RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG
This work addresses the need for more accurate and generalizable detection of atrial fibrillation and atrial flutter episodes in ECG recordings, which is incremental as it builds on existing deep learning approaches by incorporating additional morphological features.
The paper tackled the problem of atrial fibrillation detection by developing RawECGNet, a deep learning model that uses raw ECG data to exploit both rhythm and morphological information, achieving F1 scores of 0.91-0.94 on external datasets compared to 0.89-0.91 for a state-of-the-art rhythm-based model.
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.