Shape-preserving preprocessing for human pulse signals based on adaptive parameter determination.

Area of Science:

• Biomedical Engineering
• Traditional Chinese Medicine
• Signal Processing

Background:

• The human pulse signal is crucial for medical diagnosis in Traditional Chinese Medicine (TCM).
• Automating pulse signal analysis can improve diagnostic accuracy and efficiency.
• Existing methods struggle with baseline distortion and background noise in pulse waveforms.

Purpose of the Study:

• To develop a novel preprocessing method for human pulse signals.
• To improve feature extraction and classification accuracy for automated pulse diagnosis.
• To effectively remove baseline distortion and residual noise while preserving key waveform characteristics.

Main Methods:

• Utilized the dual-tree complex wavelet transform (DT-CWT) and cubic spline interpolation to remove baseline distortion.
• Implemented a two-stage filtering process: an adaptive mean filter and a second DT-CWT pass with novel thresholding.
• Employed a chain code and DT-CWT to automatically determine the adaptive mean filter's window duration.

Main Results:

• Successfully removed baseline distortion and residual background noise from the human pulse signal.
• Demonstrated excellent preservation of pulse peak information, crucial for accurate analysis.
• The preprocessing method significantly improved the quality of the waveform for feature extraction.

Conclusions:

• The proposed signal preprocessing technique effectively addresses noise and distortion in human pulse signals.
• This method enhances the potential for automated, accurate pulse-based medical diagnosis.
• Preservation of essential pulse waveform features is key to the method's success.