Digital health · 2026
A novel machine learning procedure to detect and remove artefacts in heart rate data obtained from photoplethysmography wearables: A prospective cohort study.
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Vermunicht Paulien, Buyck Christophe, Naessens Sebastiaan, Hens Wendy, Van Craenenbroeck Emeline, Piedrahita Giraldo Juan Sebastian, Makayed Katsiaryna, Herman Saartje, Laukens Kris, Roeykens Johan, De Deckere Koen, Desteghe Lien, Heidbuchel Hein
Аннотация
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% (n = 58; 4,144,654 datapoints) used for training and 25% (n = 20; 992,180 datapoints) for testing. The artefact model detected artefacts with a sensitivity of 58.5% in daily life and 76.6% during exercise, while limiting incorrect removals (74.9-91.3% specificity). Rejecting data for AAI-calculation when artefact probability was >50% and activity probability <70% produced the highest agreement with reference AAI (Pearson's r = .89-.96).
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.
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% (n = 58; 4,144,654 datapoints) used for training and 25% (n = 20; 992,180 datapoints) for testing. The artefact model detected artefacts with a sensitivity of 58.5% in daily life and 76.6% during exercise, while limiting incorrect removals (74.9-91.3% specificity). Rejecting data for AAI-calculation when artefact probability was >50% and activity probability <70% produced the highest agreement with reference AAI (Pearson's r = .89-.96).
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.