Study of the Efficiency of Biomedical Signal Filtering Algorithms in Real Time

The quality of biomedical signals collected by wearable devices is crucial for accurate physiological monitoring and detecting potential deviations. This study evaluates the effectiveness of various filtering algorithms for photoplethysmographic (PPG) signals, including low-pass, moving average, weighted average, and Kalman filters, to determine the optimal method for noise reduction while preserving key parameters such as heart rate (HR) and blood oxygen saturation (SpO₂). Data were collected using an ESP32 microcontroller and MAX30102 sensor in stationary and motion conditions. Noise levels were analyzed over 20-second intervals with high artifact presence. The results indicate that the Kalman filter achieves the best balance between noise suppression and signal integrity, preserving peak components and ensuring accurate physiological readings (HR: 87.06 bpm, SpO₂: 85.37%). In contrast, the low-pass filter excessively smooths peaks, affecting accuracy, while the moving and weighted average filters offer moderate noise reduction with varying adaptability. The findings highlight the importance of selecting appropriate filtering techniques based on computational constraints and noise levels. The Kalman filter is preferred for real-time monitoring in dynamic conditions, while simpler filters may suffice in lower-noise environments. Future research could explore machine learning-driven adaptive filtering for enhanced diagnostic accuracy and real-time medical applications.