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Modified Log-LMS adaptive filter with low signal distortion for biomedical applications
Yuzhong Jiao1, Rex Y P Cheung, Mark P C Mok
1Hong Kong Applied Science and Technology Research Institute ASTRI, Hong Kong, China. yzjiao@astri.org
Summary
A modified Log-LMS algorithm enhances noise cancellation for biomedical signals like electrocardiography (ECG). This adaptive filtering method reduces distortion and improves signal processing in noisy environments.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Adaptive Filtering
Background:
- Weak biological signals (e.g., ECG) are vulnerable to noise.
- Adaptive filters, particularly the Least Mean Squares (LMS) algorithm, are used for noise cancellation.
- The Log-LMS algorithm offers reduced complexity but can cause signal distortion.
Purpose of the Study:
- To present a modified Log-LMS algorithm for improved biomedical signal processing.
- To address the signal distortion issue inherent in standard LMS and Log-LMS algorithms.
- To evaluate the modified algorithm's performance in realistic noisy biomedical scenarios.
Main Methods:
- Development of a modified Log-LMS algorithm with a two-stage convergence process.
- Application of distinct quantization methods in each stage of the algorithm.
- Simulation analysis using two specific biomedical applications: helicopter stethoscope data and ECG with power line interference.
Main Results:
- The modified Log-LMS algorithm demonstrates fast convergence.
- The proposed method achieves significantly lower signal distortion compared to existing algorithms.
- Effective noise reduction was observed in simulated periodic life signals.
Conclusions:
- The modified Log-LMS algorithm offers a superior approach for adaptive filtering of biomedical signals.
- This algorithm effectively balances computational efficiency with signal fidelity.
- It shows promise for applications requiring robust noise cancellation in challenging environments.
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