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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.06158 |
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| _version_ | 1866909830039470080 |
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| author | Watanabe, Yuna Yamane, Natasha Sathyanarayana, Aarti Mishra, Varun Goodwin, Matthew S. |
| author_facet | Watanabe, Yuna Yamane, Natasha Sathyanarayana, Aarti Mishra, Varun Goodwin, Matthew S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Motion Artifacts: Optimizing PPG Preprocessing for Accurate Pulse Rate Variability Estimation Watanabe, Yuna Yamane, Natasha Sathyanarayana, Aarti Mishra, Varun Goodwin, Matthew S. Other Computer Science 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. |
| title | Beyond Motion Artifacts: Optimizing PPG Preprocessing for Accurate Pulse Rate Variability Estimation |
| topic | Other Computer Science |
| url | https://arxiv.org/abs/2510.06158 |