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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Bibliographic Details
Published in:Computers in biology and medicine Vol. 188; p. 109818
Main Authors: Li, Shun, Li, Juan, Mao, Jiandong, Hong, Wei, Sun, Ao
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.04.2025
Elsevier Limited
Subjects:
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
Online Access:Get full text
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Summary: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 information within the signals. Removing noise from ECG signals is an urgent problem. This paper proposes a noise-reduction method for low-frequency ECG signals using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Tuna Swarm Optimization (TSO), and Stacked Sparse Autoencoder (SSAE), named CEEMDAN-TSO-SSAE. The TSO algorithm optimizes three parameters of the CEEMDAN algorithm: Noise Standard Deviation, Number of Realizations, and Maximum Iterations. These optimized parameters are then applied to decompose the ECG signals using CEEMDAN, and the Intrinsic Mode Functions (IMFs) are obtained. The correlation coefficient method is used to screen the IMFs, excluding modal components that do not meet the threshold. Finally, each effective IMF is denoised separately using the SSAE algorithm, and the denoised effective IMFs are used for signal reconstruction. To validate the effectiveness of CEEMDAN-TSO-SSAE, its performance is compared with wavelet packet decomposition, Empirical Mode Decomposition, TSO-based Variational Mode Decomposition, and a Denoising Autoencoder algorithm. The noise-reduction method using CEEMDAN-TSO-SSAE achieves the highest Signal-to-Noise Ratio (SNR) of 19.88 and the lowest Mean Squared Error (MSE) of 0.02. In tests using real signals with baseline drift, the CEEMDAN-TSO-SSAE method again produces the highest SNR (20.25) and the lowest MSE (0.01). The results demonstrate that the proposed method outperforms the comparative algorithms, effectively eliminating complex noise in ECG signals while preserving the useful components. •An CEEMDAN-TSA-SSAE denoising algorithm is proposed for ECG signal.•The denoising algorithm introduce the stacked sparse autoencoder.•The denoising algorithm can effectively eliminate the complex ECG noise.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.109818