Beyond Motion Artifacts: Optimizing PPG Preprocessing for Accurate Pulse Rate Variability Estimation

2025-10-07

AI Summary

This study addresses high heartbeat detection errors and inaccurate pulse rate variability (PRV) and interbeat interval (IBI) estimation in photoplethysmography (PPG) signal preprocessing under low-motion conditions, where conventional fixed-bandpass filtering is suboptimal. We identify a fundamental limitation of static filter parameters: optimal cutoff frequencies vary significantly across individuals and cognitive tasks, as revealed by analyzing the relationship between signal quality and beat localization error. To overcome this, we propose a personalized, task-adaptive filtering strategy that dynamically optimizes bandpass parameters based on single-acquisition signal characteristics. Experimental results demonstrate that our method improves beat localization accuracy by 7.15% over fixed-bandwidth filtering, reduces mean absolute IBI estimation error by 35 ms, and decreases PRV time-domain metric error by 145 ms. These improvements substantially enhance the reliability and generalizability of HRV measurements in real-world low-motion scenarios.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Privacy-enhancing technologies

Abstract

Wearable physiological monitors are ubiquitous, and photoplethysmography (PPG) is the standard low-cost sensor for measuring cardiac activity. Metrics such as inter-beat interval (IBI) and pulse-rate variability (PRV) -- core markers of stress, anxiety, and other mental-health outcomes -- are routinely extracted from PPG, yet preprocessing remains non-standardized. Prior work has focused on removing motion artifacts; however, our preliminary analysis reveals sizeable beat-detection errors even in low-motion data, implying artifact removal alone may not guarantee accurate IBI and PRV estimation. We therefore investigate how band-pass cutoff frequencies affect beat-detection accuracy and whether optimal settings depend on specific persons and tasks observed. We demonstrate that a fixed filter produces substantial errors, whereas the best cutoffs differ markedly across individuals and contexts. Further, tuning cutoffs per person and task raised beat-location accuracy by up to 7.15% and reduced IBI and PRV errors by as much as 35 ms and 145 ms, respectively, relative to the fixed filter. These findings expose a long-overlooked limitation of fixed band-pass filters and highlight the potential of adaptive, signal-specific preprocessing to improve the accuracy and validity of PPG-based mental-health measures.

Problem

• Optimizing PPG band-pass filters for accurate pulse rate variability
• Addressing beat-detection errors beyond motion artifact removal
• Developing adaptive preprocessing methods for individual-specific contexts

Innovation

• Adaptive band-pass filters replace fixed preprocessing
• Person-specific cutoff tuning boosts beat detection accuracy
• Task-context optimization reduces pulse rate variability errors