A Supervised Approach to Robust Photoplethysmography Quality Assessment

Tania Pereira, Kais Gadhoumi, Mitchell Ma, Xiuyun Liu, Ran Xiao, Rene A. Colorado, Kevin J. Keenan, Karl Meisel, Xiao Hu

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Article number8681077
Pages (from-to)649-657
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number3
StatePublished - Mar 2020
Externally publishedYes


  • Annotated Data
  • Atrial Fibrillation
  • Pulse Oximetry
  • Signal Quality
  • Stroke
  • Supervised Machine Learning

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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