Motion and Noise Artifact Detection in Smartphone Photoplethysmograph Signals Using Personalized Classifier

Fatemehsadat Tabei, Behnam Askarian, Jo Woon Chong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Health parameters such as heart rhythm, blood pressure, and the level of oxygen saturation in the blood could be measured with photoplethysmography (PPG) signal. The advent of smartphone camera sensors has enabled the extraction of PPG signals from smartphones. PPG signals are weak at motion and noise artifacts (MNA) which could generate unreliable heart rate measurement. Smartphone PPG signals are more prone to MNA since they are not designed for clinical applications. PPG signals are known as biometric signals since they have unique behaviors for each individual. However, in previous MNA detection studies this personalized characteristic has not been considered. In this paper, we propose a novel personalized MNA detection method by applying a probabilistic neural network as a classifier. The performance of our personalized method is evaluated with 25 volunteered subjects in terms of accuracy, specificity, and sensitivity and compared with the generalized method. The average accuracy of our personalized method is 97.96% while it is 92.94% in the generalized one. The average values of personalized specificity and sensitivity are 99.69% and 93.91% while the generalized classifier gives 95.38% and 87.4%.

Original languageEnglish
Title of host publication2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-8
Number of pages4
ISBN (Electronic)9781728138121
DOIs
StatePublished - Nov 2019
Event2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019 - Bethesda, United States
Duration: Nov 20 2019Nov 22 2019

Publication series

Name2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019

Conference

Conference2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
CountryUnited States
CityBethesda
Period11/20/1911/22/19

Keywords

  • Motion Noise Artifacts
  • Neural Network
  • PPG
  • Personalized
  • Photoplethysmography

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