A performance analysis of convolutional autoencoder modified WaveGAN architectures for realistic 12 lead electrocardiogram synthesis

Abstract The burgeoning necessity for copious and diverse electrocardiogram (ECG) datasets for deep learning applications in clinical diagnostics has been impeded by the confidential nature of patient data. Related works have shown the effectiveness of additional data generation in enhancing the dee...

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Hlavní autoři:	Jiaqi Liu, Kwok Tai Chui, Lap-Kei Lee, Kenneth Lo, Aimin Yang, Edmond King Sing Fong
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Nature Portfolio 2025-10-01
Edice:	Scientific Reports
Témata:	12-Lead ECG synthesis Convolutional autoencoder Deep learning ECG data generation Feature extraction Generative adversarial networks
On-line přístup:	https://doi.org/10.1038/s41598-025-20470-3
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