A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection.

Journal: ...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies
Published Date: Dec 1, 2021

Abstract

Recent advances in Critical Congenital Heart Disease (CCHD) research using Photoplethysmography (PPG) signals have yielded an Internet of Things (IoT) based enhanced screening method that performs CCHD detection comparable to SpO2 screening. The use of PPG signals, however, poses a challenge due to its measurements being prone to artifacts. To comprehensively study the most effective way to remove the artifact segments from PPG waveforms, we performed feature engineering and investigated both Machine Learning (ML) and rule based algorithms to identify the optimal method of artifact detection. Our proposed artifact detection system utilizes a 3-stage ML model that incorporates both Gradient Boosting (GB) and Random Forest (RF). The proposed system achieved 84.01% of Intersection over Union (IoU), which is competitive to state-of-the-art artifact detection methods tested on higher resolution PPG.

Authors

  • Luca Cerny Oliveira
    Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.
  • Zhengfeng Lai
    Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.
  • Wenbo Geng
    Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.
  • Heather Siefkes
    Pediatrics, University of California, Davis, Sacramento, CA, USA.
  • Chen-Nee Chuah
    Department of Electrical and Computer Engineering University of California Davis California USA.

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