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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 3; p. 801
Main Authors: Shaheen, Ahmed, Ye, Liang, Karunaratne, Chrishni, Seppänen, Tapio
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.02.2025
MDPI
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25030801