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Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three­lead signals.
Wang, Liang-Hung; Zou, Yu-Yi; Xie, Chao-Xin; Yang, Tao; Abu, Patricia Angela R.
Afiliación
  • Wang LH; Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, China.
  • Zou YY; Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, China.
  • Xie CX; Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, China.
  • Yang T; Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, China. Electronic address: yangtao_fzu@fzu.edu.cn.
  • Abu PAR; Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City, Philippines.
J Electrocardiol ; 84: 27-31, 2024.
Article en En | MEDLINE | ID: mdl-38479052
ABSTRACT

BACKGROUND:

In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes due to considerations, such as portability and battery life. This situation leads to a contradiction between the demand for standard 12­lead ECG information and the limited number of leads collected by portable devices.

METHODS:

This study introduces a composite ECG vector reconstruction network architecture based on convolutional neural network (CNN) combined with recurrent neural network by using leads I, II, and V2. This network is designed to reconstruct three­lead ECG signals into 12­lead ECG signals. A 1D CNN abstracts and extracts features from the spatial domain of the ECG signals, and a bidirectional long short-term memory network analyzes the temporal trends in the signals. Then, the ECG signals are inputted into the model in a multilead, single-channel manner.

RESULTS:

Under inter-patient conditions, the mean reconstructed Root mean squared error (RMSE) for precordial leads V1, V3, V4, V5, and V6 were 28.7, 17.3, 24.2, 36.5, and 25.5 µV, respectively. The mean overall RMSE and reconstructed Correlation coefficient (CC) were 26.44 µV and 0.9562, respectively.

CONCLUSION:

This paper presents a solution and innovative approach for recovering 12­lead ECG information when only three­lead information is available. After supplementing with comprehensive leads, we can analyze the cardiac health status more comprehensively across 12 dimensions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Estudios de Factibilidad / Electrocardiografía / Aprendizaje Profundo Límite: Humans Idioma: En Revista: J Electrocardiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
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: Procesamiento de Señales Asistido por Computador / Estudios de Factibilidad / Electrocardiografía / Aprendizaje Profundo Límite: Humans Idioma: En Revista: J Electrocardiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos