[Research on PPG Signal Reconstruction Based on Compressed Sensing].

Area of Science:

• Biomedical Signal Processing
• Data Compression
• Algorithm Development

Background:

• Dynamic photoplethysmography (PPG) signals contain redundant information, impacting storage and transmission efficiency.
• Existing signal processing methods may lack accuracy in sparsity estimation and noise handling for PPG data.

Purpose of the Study:

• To propose a Modified Adaptive Matching Pursuit (MAMP) algorithm for efficient PPG signal processing.
• To enhance the accuracy and speed of PPG signal sparsity estimation.
• To improve the anti-noise performance of PPG signal reconstruction.

Main Methods:

• Developed a Modified Adaptive Matching Pursuit (MAMP) algorithm, building upon the Sparse Adaptive Matching Pursuit (SAMP) reconstruction method.
• Incorporated variable step size and double threshold conditions to improve signal sparsity estimation accuracy.
• Validated the algorithm using both simulated and actual PPG signals.

Main Results:

• The MAMP algorithm accurately and rapidly estimates PPG signal sparsity.
• Demonstrated superior anti-noise performance compared to existing methods.
• Achieved faster reconstruction speeds and higher accuracy than SAMP and Orthogonal Matching Pursuit (OMP).

Conclusions:

• The MAMP algorithm offers significant improvements in PPG signal storage and transmission efficiency.
• MAMP provides a robust and accurate method for PPG signal analysis, particularly in noisy environments.
• This algorithm presents a valuable advancement for biomedical signal processing applications.