ArticleScientific reports2025
Comparative evaluation of filtration techniques for ECG signal denoising with emphasis on stationary wavelet transform.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Physiological Monitoring Applications of Wearable Multimodal Fusion Systems Based on ECG and PPG: A Comprehensive Review.Sensors (Basel, Switzerland) · 2026Review
- Machine learning-based stratification of chagas heart failure severity using ECG power spectral biomarkers.Medical & biological engineering & computing · 2026Article
- HPRNet: a hierarchical pyramidal residual network for ECG arrhythmia classification.Frontiers in physiology · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper explores filtration techniques for processing ECG signals, focusing on the evaluation of effective denoising methods. We highlight the effectiveness of Stationary Wavelet Transform as the most suitable approach for denoising ECG signals while preserving critical cardiac features. Stationary Wavelet Transform's superior performance was validated through rigorous testing, making it a very good choice for ECG signal filtration. We have also investigated other filtration techniques, including high-pass, Chebyshev type II, Kalman, notch, Savitzky-Golay Smoothing, Moving Average, Empirical Mode Decomposition, Empirical Wavelet Transform, and Stationary Wavelet Transform filtering (with tested wavelets 'db4', 'db5', 'db6', 'sym4', 'sym5', 'coif3', 'coif4', 'coif5', 'bior3.5', and 'rbio3.9'), to assess their effectiveness in ECG signal processing. The paper highlights the advantages of the Stationary Wavelet Transform filtration technique in maintaining signal integrity for subsequent analysis. The optimal configuration, determined to be the wavelet type 'rbio3.9' at level 5 with a threshold scale of 0.5, balances effective noise reduction with the preservation of crucial ECG signal details, thereby significantly contributing to clinical ECG analysis and advancing diagnostic precision.
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