Key result
Novel ALDTF method outperforms existing techniques in denoising ECG and cardiopulmonary signals.
The proposed ALDTF algorithm effectively denoises ECG and multi-mode optical fiber cardiorespiratory signals by dynamically adapting layer-specific threshold functions, surpassing existing wavelet methods while preserving diagnostic features such as QRS complexes and ST segments.
May aid ECG monitoring by preserving diagnostic features; extends wavelet methods but remains hypothesis-generating pending clinical validation.
This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal's normalized autocorrelation function to adaptively determine the optimal threshold for each decomposition layer. The core idea applies soft thresholding at lower layers (high-frequency noise) to suppress pseudo-Gibbs oscillations, and hard thresholding at higher layers (low-frequency noise) to preserve signal amplitude and morphology. The experimental results show that for ECG signals contaminated with baseline wander (BW), electrode motion (EM) artifacts, muscle artifacts (MA), and mixed (MIX) noise, ALDTF outperforms existing methods-including SWT, DTCWT, and hybrid approaches-across multiple metrics. It achieves a ΔSNR improvement of 1.68-10.00 dB, ΔSINAD improvement of 1.68-9.98 dB, RMSE reduction of 0.02-0.56, and PRD reduction of 2.88-183.29%. The method also demonstrates excellent performance on real ECG and optical fiber cardiopulmonary signals, preserving key diagnostic features like QRS complexes and ST segments while effectively suppressing artifacts. ALDTF provides an efficient, versatile solution for physiological signal denoising with strong potential in wearable real-time monitoring systems.
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Zhang et al. (2025) studied Electrocardiogram and cardiorespiratory signal noise. Adaptive Layer-Dependent Threshold Function (ALDTF) for wavelet denoising vs. Existing methods (including SWT, DTCWT, and hybrid approaches) was evaluated on Signal quality improvement (ΔSNR, ΔSINAD, RMSE, PRD). The adaptive layer-dependent threshold function (ALDTF) outperformed existing methods in denoising ECG and cardiopulmonary signals, achieving a ΔSNR improvement of 1.68-10.00 dB and RMSE reduction of 0.02-0.56.
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