Algorithm for Blood Oxygen Measurement

Blood oxygen measurement algorithms are typically based on photoplethysmography (PPG) signal processing, estimating oxygen saturation (SpO2) by analyzing light absorption variations at two wavelengths: red and infrared light. The algorithm's core components involve signal preprocessing, feature extraction, and ratio calculation.

In C language implementations, the algorithm generally follows these steps: First, raw PPG signals undergo filtering to eliminate baseline drift and high-frequency noise, achievable through moving average filters or digital filter implementations. Subsequently, peak detection algorithms identify characteristic points of pulse waves, calculating the ratio (R-value) of AC to DC components for both red and infrared signals. Finally, empirical formulas convert the R-value to SpO2 readings, with these formulas typically calibrated through clinical data.

Matlab implementations focus more on rapid algorithm validation and data analysis. Leveraging Matlab's powerful signal processing toolbox, advanced techniques like wavelet denoising and FFT spectral analysis can be easily implemented. Using built-in functions such as findpeaks() for peak detection and matrix operations enables efficient R-value computation and real-time waveform plotting. The Matlab version's advantage lies in convenient visualization of intermediate results, such as comparing pre- and post-filtering signals or evaluating performance curves of different algorithms.

The key differences between implementations are: C language suits real-time embedded system processing better, requiring manual memory management and computational optimization; while Matlab emphasizes algorithm prototyping, utilizing ready-made functions for rapid design iteration. Practical applications often use Matlab for parameter determination before porting to C language for hardware deployment.