ArticleDigital health
A novel machine learning procedure to detect and remove artefacts in heart rate data obtained from photoplethysmography wearables: A prospective cohort study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Heart Rate Monitors for the Estimation of Physical Activity in Patients With Cardiovascular Disease: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- Autonomic Nervous Dysfunction and Ultra-Short-Term Heart Rate Variability in Atrial Fibrillation: Recent Advances in Early Detection.Journal of cardiovascular development and disease · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Photoplethysmography (PPG) based heart rate (HR) monitoring supports continuous assessment of physical activity intensity, but device generated HR values remain prone to motion artefacts. This study validated a novel machine learning procedure that detects artefacts directly in PPG derived HR data, rather than in raw waveforms that are unavailable in commercial devices. Methods: In this prospective study, 149 participants (46 following cardiac rehabilitation, 103 healthy) wore a PPG-based wrist device and a reference chest strap for 12 weeks. Prior testing defined participants as "PPG-compatible" (i.e., HR error <10% during ≥70% of training data). Three quarters trained artefact and activity detection models, one quarter served as the test population. To balance artefact removal and activity preservation, multiple probability thresholds were combined to reject unreliable HR values, remove or interpolate using adjacent reliable values. The Antwerp Activity Index (AAI), an HR-based PA score, was calculated from the resulting PPG and reference data to evaluate threshold combinations. Results: Seventy-eight participants (53.8%) were PPG-compatible, yielding over 5 million datapoints, with 75% ( Conclusions: This new machine learning-based procedure, which operates on device-generated HR rather than PPG waveforms, effectively removes artefacts while preserving key activity signals, ensuring clinically relevant PA assessment in daily life.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.