Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals

Cardiovascular diseases (CVDs) are the primary cause of death worldwide. For accurate diagnosis of CVDs, robust and efficient ECG denoising is particularly critical in ambulatory cases where various artifacts can degrade the quality of the ECG signal. None of the present denoising methods preserve t...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 25; H. 3; S. 801
Hauptverfasser:	Shaheen, Ahmed, Ye, Liang, Karunaratne, Chrishni, Seppänen, Tapio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.02.2025 MDPI
Schlagworte:	Algorithms Analysis Approximation Cardiac stress tests Cardiovascular diseases Cardiovascular Diseases - diagnosis Cardiovascular Diseases - physiopathology convolutional neural network (CNN) Deep learning denoising autoencoder (DAE) Diffusion models Electrocardiogram electrocardiogram (ECG) Electrocardiography Electrocardiography - methods gated convolution Humans Leukocytes Morphology Neural networks Neural Networks, Computer Noise self-ONN Signal processing Signal Processing, Computer-Assisted Signal-To-Noise Ratio U-Net Wavelet transforms electrocardiogram (ECG) convolutional neural network (CNN) gated residual motion artifacts self-ONN U-Net attention denoising autoencoder (DAE) gated convolution
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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