Your browser doesn't support javascript.
loading
Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.
Kim, Jiwoong; Lee, Sun Jung; Ko, Bonggyun; Lee, Myungeun; Lee, Young-Shin; Lee, Ki Hong.
Afiliación
  • Kim J; Department of Mathematics and Statistics, Chonnam National University, Gwangju, Korea.
  • Lee SJ; Department of Cardiovascular Medicine, Chonnam National University Hospital, Gwangju, Korea.
  • Ko B; Seers Technology Co., Ltd., Pyeongtaek, Korea.
  • Lee M; Department of Mathematics and Statistics, Chonnam National University, Gwangju, Korea.
  • Lee YS; XRAI, Gwangju, Korea.
  • Lee KH; Department of Cardiovascular Medicine, Chonnam National University Hospital, Gwangju, Korea.
J Korean Med Sci ; 39(5): e56, 2024 Feb 05.
Article en En | MEDLINE | ID: mdl-38317452
ABSTRACT

BACKGROUND:

The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR.

METHODS:

We employed three deep learning models recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy.

RESULTS:

ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68.

CONCLUSION:

The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fibrilación Atrial / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Korean Med Sci Asunto de la revista: MEDICINA Año: 2024 Tipo del documento: Article Pais de publicación: Corea del Sur
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fibrilación Atrial / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Korean Med Sci Asunto de la revista: MEDICINA Año: 2024 Tipo del documento: Article Pais de publicación: Corea del Sur