Improved CEEMDAN and Adaptive Wavelet Soft Thresholding for ECG Noise Detection and Suppression

T. Raghavendra Gupta; D. Uma Nandhini

doi:10.57159/jcmm.5.3.26973

Authors

T. Raghavendra Gupta Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India https://orcid.org/0000-0002-5529-8567
D. Uma Nandhini Department of Computer Science and Engineering, School of Computing, Vel Tech University, Chennai, Tamil Nadu, India https://orcid.org/0000-0001-9972-5076

DOI:

https://doi.org/10.57159/jcmm.5.3.26973

Keywords:

Electrocardiogram, Baseline Wander, Power-line Interference, Muscle Artifacts, Empirical Mode Decomposition, Discrete Wavelet Transform

Abstract

Electrocardiogram (ECG) signals are widely used for the diagnosis of cardiovascular diseases. Their accuracy, however, is significantly affected by various noises, including baseline wander, power-line interference, and muscle artifacts. Baseline wander arises from respiration and electrode motion and produces low-frequency drift. Power-line interference originates from electromagnetic coupling and appears as a narrow-band signal at 50 Hz or 60 Hz. Muscle artifacts are non-stationary and overlap with the ECG frequency band, which makes their suppression particularly challenging. This paper proposes a hybrid framework that integrates an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) with a modified stoppage criterion and a Discrete Wavelet Transform (DWT) with adaptive soft thresholding for robust ECG denoising. The noisy ECG signal is first decomposed using the improved CEEMDAN. Noise detection is then performed using Maximum Absolute Amplitude (MAA) and Autocorrelation Maximum Amplitude (AMA) features, which classify each segment as containing baseline wander, power-line interference, or muscle artifacts. The identified noisy segments are processed using DWT with adaptive soft thresholding, which suppresses noise selectively in noise-dominan

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