A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky⁻Golay Filter for ECG Denoising
Hui Huang1, Shiyan Hu2, Ye Sun3
1Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI 49931, USA. huih@mtu.edu.
Sensors (Basel, Switzerland)
|April 17, 2019
Summary
A new low-distortion adaptive Savitzky-Golay (LDASG) filter effectively denoises electrocardiogram (ECG) signals from wearable sensors. This method reduces noise while minimizing signal distortion, outperforming existing techniques for cardiovascular disease diagnosis.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Wearable Technology
Background:
- Wearable electrocardiogram (ECG) sensors are crucial for cardiovascular disease diagnosis.
- These devices introduce noise, complicating signal interpretation.
- Existing denoising methods often distort high-variation ECG signals.
Purpose of the Study:
- To develop a novel low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising.
- To improve noise elimination while preserving signal integrity.
- To address the limitations of standard filters in handling ECG signal variations.
Main Methods:
- Proposed a low-distortion adaptive Savitzky-Golay (LDASG) filter.
- Utilized discrete curvature estimation to adapt the filter to signal variations.
- Compared LDASG against Empirical Mode Decomposition-wavelet (EMD-wavelet) and Non-Local Means (NLM) methods.
Main Results:
- LDASG demonstrated superior performance in both noise elimination and signal distortion reduction.
- Achieved a 33.33% decrease in Mean Squared Error (MSE) compared to EMD-wavelet and 50% vs. NLM.
- Reduced Percent Root-Mean-Square Deviation (PRD) by 18.25% vs. EMD-wavelet and 25.24% vs. NLM.
Conclusions:
- The LDASG filter effectively denoises ECG signals with minimal distortion.
- This method offers significant improvements over current state-of-the-art techniques.
- LDASG shows high potential for clinical and consumer wearable ECG applications.
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