[Research on heart rate extraction algorithm in motion state based on normalized least mean square combining ensemble...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Wearable Technology

Background:

• Photoplethysmographic (PPG) signals are susceptible to motion artifacts, high-frequency noise, and baseline drift, complicating accurate heart rate monitoring during physical activity.
• Existing de-noising methods often struggle to effectively remove these interferences simultaneously, limiting the reliability of PPG-based heart rate measurements in real-world scenarios.

Purpose of the Study:

• To develop and validate a robust de-noising method for PPG signals that effectively removes motion artifacts, high-frequency noise, and baseline drift.
• To enable accurate heart rate calculation from PPG signals even under dynamic motion conditions.
• To enhance the utility of PPG for physiological monitoring during human movement.

Main Methods:

• A novel de-noising approach combining normalized least mean square (NLMS) adaptive filtering with ensemble empirical mode decomposition (EEMD) was proposed.
• Motion artifacts were filtered using an NLMS adaptive filter, referencing a 3-axis accelerometer.
• High-frequency noise and baseline drift were subsequently removed by decomposing the signal with EEMD and applying a permutation entropy (PE) criterion for IMF selection.

Main Results:

• The proposed method demonstrated significant noise reduction in PPG signals during motion.
• A high Pearson correlation coefficient of 0.731 was achieved between the calculated heart rate and the standard heart rate from ECG.
• The average absolute error percentage for heart rate calculation was 6.10% under various motion states.

Conclusions:

• The integrated EEMD and NLMS adaptive filtering method effectively de-noises PPG signals, allowing for accurate heart rate estimation in motion.
• This approach significantly improves the reliability of PPG-based heart rate monitoring for physiological applications during physical activity.
• The method shows promise for enhancing wearable health monitoring systems and real-time physiological assessment during human motion.