A denoising method for ECG signals based on CEEMDAN-TSO and stacked sparse autoencoders

Electrocardiogram (ECG) signals are used to detect the health status of the heart, providing an important basis for the prevention and diagnosis of cardiovascular diseases. However, ECG signals are susceptible to environmental and equipment-related influences, which can obscure the characteristic in...

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Vydané v:	Computers in biology and medicine Ročník 188; s. 109818
Hlavní autori:	Li, Shun, Li, Juan, Mao, Jiandong, Hong, Wei, Sun, Ao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.04.2025 Elsevier Limited
Predmet:	Algorithms Autoencoder Cardiomyocytes Cardiovascular diseases Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) Correlation coefficients Deep learning Effectiveness EKG Electrocardiogram (ECG) Electrocardiography Electrocardiography - methods Fourier transforms Heart Human body Humans Internal Medicine Methods Noise reduction Noise standards Optimization Other Parameters Signal processing Signal Processing, Computer-Assisted Signal reconstruction Signal to noise ratio Stacked sparse autoencoder (SSAE) Tuna swarm optimization (TSO) Wavelet transforms Tuna swarm optimization (TSO) Stacked sparse autoencoder (SSAE) Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) Electrocardiogram (ECG) Noise reduction
ISSN:	0010-4825, 1879-0534, 1879-0534
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