Motion artifact reduction in photoplethysmogram signals based on Periodic Component Factorization

Area of Science:

• Biomedical Engineering
• Signal Processing
• Physiological Monitoring

Background:

• Continuous blood pressure measurement via pulse transit time (PTT) is a significant research area.
• Existing algorithms provide accurate estimations primarily in stationary conditions.
• Hand movement and strenuous exercise introduce motion artifacts (MA) that compromise blood pressure accuracy.

Purpose of the Study:

• To introduce a novel algorithm, Periodic Component Factorization (PCF), for enhanced motion artifact removal from photoplethysmography (PPG) signals.
• To address the limitations of current methods in handling dynamic physical activity.
• To improve the reliability of continuous blood pressure estimation during movement.

Main Methods:

• Development of Periodic Component Factorization (PCF) as an extension of Independent Component Analysis (ICA).
• Application of PCF to photoplethysmography (PPG) signals contaminated with motion artifacts.
• Comparison of PCF with existing algorithms like FastICA, focusing on signal periodicity and quasi-periodicity.

Main Results:

• PCF demonstrates superior performance in extracting dependent source components from noisy PPG signals.
• The algorithm effectively removes motion artifacts, particularly in signals exhibiting quasi-periodicity or periodicity.
• PCF shows practical effectiveness in mitigating motion-induced inaccuracies in PPG-based measurements.

Conclusions:

• Periodic Component Factorization (PCF) offers a robust solution for motion artifact removal in PPG signals.
• This advancement is crucial for accurate continuous blood pressure monitoring during physical activity.
• The PCF algorithm enhances the feasibility of reliable, non-invasive blood pressure estimation in real-world, dynamic scenarios.