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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 25; číslo 3; s. 801
Hlavní autoři:	Shaheen, Ahmed, Ye, Liang, Karunaratne, Chrishni, Seppänen, Tapio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 01.02.2025 MDPI
Témata:	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
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Popis
Shrnutí:	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 the morphology of ECG signals adequately for all noise types, especially at high noise levels. This study proposes a novel Fully-Gated Denoising Autoencoder (FGDAE) to significantly reduce the effects of different artifacts on ECG signals. The proposed FGDAE utilizes gating mechanisms in all its layers, including skip connections, and employs Self-organized Operational Neural Network (self-ONN) neurons in its encoder. Furthermore, a multi-component loss function is proposed to learn efficient latent representations of ECG signals and provide reliable denoising with maximal morphological preservation. The proposed model is trained and benchmarked on the QT Database (QTDB), degraded by adding randomly mixed artifacts collected from the MIT-BIH Noise Stress Test Database (NSTDB). The FGDAE showed the best performance on all seven error metrics used in our work in different noise intensities and artifact combinations compared with state-of-the-art algorithms. Moreover, FGDAE provides reliable denoising in extreme conditions and for varied noise compositions. The significantly reduced model size, 61% to 73% reduction, compared with the state-of-the-art algorithm, and the inference speed of the FGDAE model provide evident benefits in various practical applications. While our model performs best compared with other models tested in this study, more improvements are needed for optimal morphological preservation, especially in the presence of electrode motion artifacts.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s25030801