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Reconstruct 12-lead ECG from II-lead： based on an improved generative adversarial network model

Year: 2025

Type: article

Abstract: Electrocardiogram (ECG) provides essential clues for detecting heart diseases, and more and more smart devices are developed to monitor the ECG signals. However, due to the convenience of use, only limited leads of ECG signals are measured from such smart devices, which may significantly affect the validity of disease judgement. To enhance the diagnose performance of these smart devices, this study suggests a Generative Adversarial Network (GAN)-based approach that combines Transformer and U-Net structures to autonomously reconstruct the whole 12-lead ECG signals from a single lead ECG signals. This study evaluates the proposed model on the PTB-XL and the Shaoxing People's Hospital 12-lead ECG dataset, then compares it with several state of art approaches. The code for this study can be found in https://github.com/Chaoquan-123/12-lead-ECG-reconstruction.

Source: DOAJ (DOAJ: Directory of Open Access Journals)

DOI: 10.16157/j.issn.0258-7998.245796

Authors: Zeng Chaoquan, Luo Wei, Wang Senlin, Dai Lingfeng, Chen Hao

Institutions: Fuzhou University, Chinese Academy of Sciences, Fujian Institute of Research on the Structure of Matter

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6962548041

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