Key result
Novel adaptive Savitzky-Golay filter cuts ECG denoising error by up to ~50% versus standard methods.
Why the study?
Wearable ECG sensors introduce additional noise contamination, and existing ECG denoising methods often induce signal distortion for high-variation signals such as ECG.
Does the LDASG filtering method improve ECG denoising and reduce signal distortion compared to EMD-wavelet and NLM methods?
Does the LDASG filtering method improve ECG denoising and reduce signal distortion compared to EMD-wavelet and NLM methods?
Effect estimate: decreases in MSE by 33.33% to 50% and PRD by 18.25% to 25.24%
A novel adaptive Savitzky-Golay filter based on discrete curvature estimation significantly reduces signal distortion in ECG denoising compared to existing methods, showing potential for wearable ECG sensors.
May enhance wearable ECG denoising; leaves open prospective clinical validation before practice adoption.
Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.
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Huang et al. (2019) studied ECG denoising. Low-distortion adaptive Savitzky-Golay (LDASG) filtering method vs. EMD-wavelet based method and non-local means (NLM) denoising method was evaluated on Signal distortion reduction (MSE and PRD) (decreases in MSE by 33.33% to 50% and PRD by 18.25% to 25.24%). The proposed low-distortion adaptive Savitzky-Golay filter decreased mean squared error by up to 50% and percent root mean square difference by up to 25.24% compared to standard denoising methods.
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