Algorithms for Blood Oxygen Measurement: C and MATLAB Implementations

This article provides a comprehensive exploration of blood oxygen measurement algorithm implementations using two distinct programming languages: C and MATLAB. The implementation of blood oxygen measurement algorithms requires specialized techniques and algorithms, including infrared-based photoplethysmography (PPG) signal acquisition, signal processing, and filtering operations. The approaches for implementing these techniques differ significantly between C and MATLAB, and we will examine both implementations separately. In C implementations, developers typically manage memory allocation manually and optimize real-time processing through efficient pointer manipulation and low-level hardware access. MATLAB implementations, conversely, leverage built-in signal processing toolboxes with functions like filtfilt() for zero-phase filtering and findpeaks() for pulse detection, enabling rapid prototyping with high-level mathematical operations. We will also discuss optimization strategies for improving algorithm accuracy and computational efficiency, such as implementing moving average filters in C using circular buffers or applying wavelet denoising in MATLAB for motion artifact reduction. The article concludes by summarizing key findings and providing practical recommendations to help readers better understand and apply blood oxygen measurement algorithms in their projects.