Photoplethysmograph signal reconstruction based on a novel hybrid motion artifact detection–reduction approach. Part I: Motion and noise artifact detection
Abstract
Motion and noise artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA detection method which can provide a clean vs. corrupted decision on each successive PPG segment. For motion artifact detection, we compute four time-domain parameters: (1) standard deviation of peak-to-peak intervals (2) standard deviation of peak-to-peak amplitudes (3) standard deviation of systolic and diastolic interval ratios, and (4) mean standard deviation of pulse shape. We have adopted a support vector machine (SVM) which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them. We apply several distinct features of the PPG data to enhance classification performance. The algorithm we developed was verified on PPG data segments recorded by simulation, laboratory-controlled and walking/stair-climbing experiments, respectively, and we compared several well-established MNA detection methods to our proposed algorithm. All compared detection algorithms were evaluated in terms of motion artifact detection accuracy, heart rate (HR) error, and oxygen saturation (SpO2) error. For laboratory controlled finger, forehead recorded PPG data and daily-activity movement data, our proposed algorithm gives 94.4, 93.4, and 93.7% accuracies, respectively. Significant reductions in HR and SpO2 errors (2.3 bpm and 2.7%) were noted when the artifacts that were identified by SVM-MNA were removed from the original signal than without (17.3 bpm and 5.4%). The accuracy and error values of our proposed method were significantly higher and lower, respectively, than all other detection methods. Another advantage of our method is its ability to provide highly accurate onset and offset detection times of MNAs. This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed, which is the subject of the companion paper to this article. Finally, our MNA detection algorithm is real-time realizable as the computational speed on the 7-s PPG data segment was found to be only 7 ms with a Matlab code.
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Chong et al. achieved an accuracy of 93.7% during a daily motion experiment and saw a significant reduction in error for SpO2 and heart rate measurements.
“They were able to achieve an accuracy of 93.7% during a daily motion experiment, and they saw a significant reduction in error for SpO2 and heart rate measurements.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [170]
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Photoplethysmography Signal Quality Assessment using Attentive-CNN Models
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Robotic surgery and cardiac biosignals: Bridging human–artificial intelligence collaboration
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Pulse‐based photoplethysmography quality assessment improves wearable seizure detection performance
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A Reflectance–based multimodal wearable photoplethysmography (PPG) sensor
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Autonomous Heart Rate Tracking Methodology Using Kalman Filter and the EM Algorithm
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Wearable Wireless Sensor for Multi-Scale Physiological Monitoring
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Telemedicine systems to manage chronic disease
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Estimation of Non-invasive Cuff-less Blood Pressure Using the Photoplethysmogram SignalPreprint
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Motion noise cancellation algorithm in PPG signals based on temporal variability analysisPreprint
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- Authors
- Chong, J. W., Dao, D. K., Salehizadeh, S. M. A., McManus, D. D., Darling, C. E., Chon, K. H., & Mendelson, Y.
- Affiliations
- Worcester Polytechnic Institute (US)
- University of Massachusetts Chan Medical School (US)
- Corresponding author
- Chon, Ki H.
- Venue
- Annals of Biomedical Engineering
- Published
- 2014-08-05
- Volume
- 42
- Issue
- 11
- Pages
- 2238-2250
- ISSN
- 0090-6964 (ISSN-L)
- 1573-9686
- OpenAlex
- W2042388577
- Cited by
- 79
- References
- 41
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 5 · 2023: 3 · 2022: 6 · 2021: 6 · 2020: 12 · 2019: 12 · 2018: 15 · 2017: 1 · 2016: 9 · 2015: 2 · 2013: 1
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Annals of Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- photoplethysmogram, motion artifact detection, support vector machine, time-domain features, heart rate estimation, oxygen saturation, vital signs monitoring, real-time processing
- MeSH terms
- Algorithms, Heart Rate, Humans, Monitoring, Physiologic, Motion, Oximetry, Artifacts, Photoplethysmography
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