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Artifact Detection of Wrist Photoplethysmograph Signals

K. Vandecasteele,J. Lázaro,E. Cleeren,Kasper Claes,W. Paesschen,S. Huffel,B. Hunyadi

Published 2018 in International Conference on Bio-inspired Systems and Signal Processing

ABSTRACT

There is a growing interest in monitoring of vital signs through wearable devices, such as heart rate (HR). A comfortable and non-invasive technique to measure the HR is pulse photoplethysmography (PPG) with the use of a smartwatch. This watch records also triaxial accelerometry (ACM). However, it is well known that motion and noise artifacts (MNA) are present. A MNA detection method, which classifies into a clean or MNA segment, is trained and tested on a dataset of 17 patients, each with a recording duration of 24 hours. PPGand ACM-derived features are extracted and classified with a LS-SVM classifier. A sensitivity and specificity of respectively 85.50 % and 92.36 % are obtained. For this dataset, the ACM features do not improve the performance, suggesting that ACM recording could be avoided from the point of view for detecting MNA in PPG signals during daily life.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    International Conference on Bio-inspired Systems and Signal Processing

  • Publication date

    Unknown publication date

  • Fields of study

    Medicine, Computer Science, Engineering

  • Identifiers
    DOI 10.5220/0006594301820189
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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