Original Program for Pulse Wave Feature Point Extraction

In the field of physiological signal processing, pulse wave feature point extraction represents a significant research direction. This type of program is commonly used to analyze key points in pulse wave signals, such as the main wave, tidal wave, and dicrotic wave characteristics, thereby providing data support for cardiovascular health assessment.

The core implementation approach generally involves the following steps: First, preprocessing of raw pulse wave signals is required, including filtering for noise removal and baseline correction to eliminate interference signals. This can be implemented using digital filters like Butterworth or moving average filters in code. Next, peak detection algorithms (such as findpeaks function in MATLAB or custom threshold-based methods) are employed to locate the main wave peak of the pulse wave. Then, first or second derivative information of the waveform is utilized to identify other characteristic points, like tidal wave and dicrotic wave positions - this typically involves calculating derivatives using gradient functions and detecting zero-crossings. Finally, validation and optimization of extracted feature points may be necessary, potentially implementing consistency checks or machine learning-based verification algorithms to ensure result accuracy.

Such programs have wide applications in medical monitoring, health wearable devices, and biological signal analysis. Interested individuals can further discuss algorithm optimization or practical application challenges through technical exchanges.