RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.
IEEE J Biomed Health Inform
; 28(9): 5180-5188, 2024 Sep.
Article
em En
| MEDLINE
| ID: mdl-38787663
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fibrilação Atrial
/
Processamento de Sinais Assistido por Computador
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Eletrocardiografia
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Aprendizado Profundo
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article