TY - JOUR
T1 - PPGMotion
T2 - Model-based detection of motion artifacts in photoplethysmography signals
AU - Maity, Akash Kumar
AU - Veeraraghavan, Ashok
AU - Sabharwal, Ashutosh
N1 - Funding Information:
This work was partially supported by NSF ERC Grant EEC-1648451 (for PATHS-UP ERC).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/5
Y1 - 2022/5
N2 - Photoplethysmography (PPG) is used widely in health wearables to monitor biomarkers like heart rate. However, motion activities degrade the quality of the measured PPG signal, thereby reducing the accuracy of heart-rate estimation. Existing state-of-the-art methods for motion detection rely on the semi-periodic structure of PPG to detect the aperiodic motion artifacts, thereby failing in scenarios when motion contamination tends to be periodic. We propose a novel technique, PPGMotion, for detecting all types of motion artifacts in PPG signals with high accuracy, without the need for any reference motion signals. Our approach relies on the morphological structure of the artifact-free PPG signal. We compare our method against some classical methods on one synthetic and four real datasets – dataset (1) and (2) are obtained from finger pulse-oximeter under motion activities, dataset (3) and (4) are obtained from a wearable smartwatch. We show that for the synthetic dataset, the performance of PPGMotion is significantly better than existing work as the contaminated PPG tends to become periodic, with an increase in sensitivity of at least 20% over state-of-the-art methods. For real data, PPGMotion achieves similar performance for random motion artifact detection as the classical methods but performs significantly better when motion tends to be periodic, with at least 10% increase in sensitivity in detecting motion artifacts in datasets (2), (3), and (4).
AB - Photoplethysmography (PPG) is used widely in health wearables to monitor biomarkers like heart rate. However, motion activities degrade the quality of the measured PPG signal, thereby reducing the accuracy of heart-rate estimation. Existing state-of-the-art methods for motion detection rely on the semi-periodic structure of PPG to detect the aperiodic motion artifacts, thereby failing in scenarios when motion contamination tends to be periodic. We propose a novel technique, PPGMotion, for detecting all types of motion artifacts in PPG signals with high accuracy, without the need for any reference motion signals. Our approach relies on the morphological structure of the artifact-free PPG signal. We compare our method against some classical methods on one synthetic and four real datasets – dataset (1) and (2) are obtained from finger pulse-oximeter under motion activities, dataset (3) and (4) are obtained from a wearable smartwatch. We show that for the synthetic dataset, the performance of PPGMotion is significantly better than existing work as the contaminated PPG tends to become periodic, with an increase in sensitivity of at least 20% over state-of-the-art methods. For real data, PPGMotion achieves similar performance for random motion artifact detection as the classical methods but performs significantly better when motion tends to be periodic, with at least 10% increase in sensitivity in detecting motion artifacts in datasets (2), (3), and (4).
KW - Morphology
KW - Motion artifacts
KW - Photoplethysmography
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U2 - 10.1016/j.bspc.2022.103632
DO - 10.1016/j.bspc.2022.103632
M3 - Article
AN - SCOPUS:85126123526
SN - 1746-8094
VL - 75
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 103632
ER -