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A Supervised Approach to Robust Photoplethysmography Quality Assessment.

Publication ,  Journal Article
Pereira, T; Gadhoumi, K; Ma, M; Liu, X; Xiao, R; Colorado, RA; Keenan, KJ; Meisel, K; Hu, X
Published in: IEEE journal of biomedical and health informatics
March 2020

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.

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Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

March 2020

Volume

24

Issue

3

Start / End Page

649 / 657

Related Subject Headings

  • Young Adult
  • Wearable Electronic Devices
  • Support Vector Machine
  • Supervised Machine Learning
  • Stroke
  • Signal Processing, Computer-Assisted
  • Photoplethysmography
  • Oximetry
  • Middle Aged
  • Humans
 

Citation

APA
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Pereira, T., Gadhoumi, K., Ma, M., Liu, X., Xiao, R., Colorado, R. A., … Hu, X. (2020). A Supervised Approach to Robust Photoplethysmography Quality Assessment. IEEE Journal of Biomedical and Health Informatics, 24(3), 649–657. https://doi.org/10.1109/jbhi.2019.2909065
Pereira, Tania, Kais Gadhoumi, Mitchell Ma, Xiuyun Liu, Ran Xiao, Rene A. Colorado, Kevin J. Keenan, Karl Meisel, and Xiao Hu. “A Supervised Approach to Robust Photoplethysmography Quality Assessment.IEEE Journal of Biomedical and Health Informatics 24, no. 3 (March 2020): 649–57. https://doi.org/10.1109/jbhi.2019.2909065.
Pereira T, Gadhoumi K, Ma M, Liu X, Xiao R, Colorado RA, et al. A Supervised Approach to Robust Photoplethysmography Quality Assessment. IEEE journal of biomedical and health informatics. 2020 Mar;24(3):649–57.
Pereira, Tania, et al. “A Supervised Approach to Robust Photoplethysmography Quality Assessment.IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 3, Mar. 2020, pp. 649–57. Epmc, doi:10.1109/jbhi.2019.2909065.
Pereira T, Gadhoumi K, Ma M, Liu X, Xiao R, Colorado RA, Keenan KJ, Meisel K, Hu X. A Supervised Approach to Robust Photoplethysmography Quality Assessment. IEEE journal of biomedical and health informatics. 2020 Mar;24(3):649–657.

Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

March 2020

Volume

24

Issue

3

Start / End Page

649 / 657

Related Subject Headings

  • Young Adult
  • Wearable Electronic Devices
  • Support Vector Machine
  • Supervised Machine Learning
  • Stroke
  • Signal Processing, Computer-Assisted
  • Photoplethysmography
  • Oximetry
  • Middle Aged
  • Humans