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Improved wavelet entropy calculation with window functions and its preliminary application to study intracranial
Peng Xu1, Xiao Hu, Dezhong Yao
1Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, Los Angeles, USA. leisure_xp@163.com
A new windowed wavelet entropy method offers stable signal complexity measurement, outperforming traditional approaches. This technique accurately distinguishes physiological states, showing promise for predicting intracranial pressure.
Area of Science:
- Signal processing
- Biomedical engineering
- Complexity science
Background:
- Wavelet entropy measures signal regularity via wavelet sub-band energy distribution.
- Existing wavelet entropy is sensitive to noise, particularly in physiological signals.
- A stable entropy calculation method is needed for physiological signal analysis.
Purpose of the Study:
- To introduce and evaluate a windowed wavelet entropy approach for enhanced signal regularity measurement.
- To systematically compare wavelet entropy with approximate entropy.
- To assess the utility of relative wavelet entropy for quantifying signal dissimilarity.
Main Methods:
- Development of a windowed wavelet entropy calculation method.
- Systematic comparison of wavelet entropy and approximate entropy across various signals.
- Application of the windowed approach to physiological time series data from patients with intracranial hypertension.
Main Results:
- Wavelet entropy demonstrates comparable signal complexity measurement to approximate entropy.
- Relative wavelet entropy effectively quantifies signal dissimilarity.
- The windowed approach yields smoother, more stable entropy calculations than the original method.
- The new method successfully differentiates normal and intracranial hypertension states in patient data.
Conclusions:
- Windowed wavelet entropy provides a more robust measure of signal regularity and dissimilarity.
- This approach offers improved stability and reliability for analyzing noisy physiological signals.
- The method shows potential as a tool for predicting intracranial pressure and monitoring patient states.
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