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Application of translation wavelet transform with new threshold function in pulse wave signal denoising
Jun Zhang1,2, Xingguang Geng1, Yitao Zhang1,2
1Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
Summary
A new pulse wave denoising algorithm using translation invariant wavelet transform (TIWT) and a novel threshold function improves signal quality. This method better preserves pulse wave characteristics for enhanced physiological and pathological analysis.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Physiological Monitoring
Background:
- Wrist pulse wave analysis is crucial for physiological and pathological detection.
- Traditional wavelet threshold filtering often fails to adequately denoise and preserve pulse wave details.
Purpose of the Study:
- To propose an improved pulse wave denoising algorithm.
- To enhance the accuracy of physiological and pathological information extraction from wrist pulse waves.
Main Methods:
- Developed a novel threshold function combining soft and hard thresholding properties using a hyperbolic tangent curve.
- Employed translation invariant wavelet transform (TIWT) to mitigate pseudo-Gibbs phenomena.
- Applied the new algorithm to denoise wrist pulse wave signals.
Main Results:
- The proposed algorithm demonstrated superior performance compared to traditional wavelet filtering.
- Achieved better preservation of pulse wave geometric characteristics.
- Resulted in a significantly higher signal-to-noise ratio (SNR).
Conclusions:
- The TIWT combined with the novel threshold function effectively addresses limitations of conventional denoising methods.
- This enhanced denoising approach provides a foundation for more accurate time-domain characteristic extraction from pulse waves.
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