A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.
Sci Rep
; 11(1): 12818, 2021 06 17.
Article
en En
| MEDLINE
| ID: mdl-34140578
ABSTRACT
Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 712 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Fibrilación Atrial
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Nodo Sinoatrial
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Algoritmos
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Electrocardiografía
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Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
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Screening_studies
Límite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Sci Rep
Año:
2021
Tipo del documento:
Article