A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection

Luca Cerny Oliveira, Zhengfeng Lai, Wenbo Geng, Heather Siefkes, Chen Nee Chuah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE/ACM Conference on Connected Health
Subtitle of host publicationApplications, Systems and Engineering Technologies, CHASE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages149-154
Number of pages6
ISBN (Electronic)9781665439657
DOIs
StatePublished - 2021
Event6th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2021 - Washington, United States
Duration: Dec 16 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2021

Conference

Conference6th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2021
Country/TerritoryUnited States
CityWashington
Period12/16/2112/18/21

Keywords

  • artifacts
  • CCHD
  • Machine Learning
  • PPG

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Medicine (miscellaneous)
  • Health Informatics
  • Health(social science)

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