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Auto-regression-driven, reallocative particle filtering approaches in PPG-based respiration rate estimation

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Details

Original languageEnglish
Title of host publicationEMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017
PublisherSpringer Verlag
Pages1020-1024
Number of pages5
ISBN (Print)9789811051210
DOIs
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventJoint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) -
Duration: 1 Jan 1900 → …

Publication series

NameIFMBE Proceedings
Volume65
ISSN (Print)1680-0737

Conference

ConferenceJoint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC)
Period1/01/00 → …

Abstract

Interest towards respiratory state assessment with non-obtrusive instrumentation has led to the design of novel algorithmic solutions. Notably, respiratory behavior has been observed to cause modulative changes in two discreetly measurable physiological signals, PPG and ECG. The potential to integrate respiratory rate measurements in widely used instrumentation with no additional cost has made the research of suitable signal processing methods attractive. We have studied and compared auto-regressive (AR) model order optimization and coefficient extraction methods combined with a reallocative particle filtering approach for respiration rate estimation from finger PPG signal. The evaluated coefficient extraction methods were Yule-Walker, Burg, and Least-square. Considered model order optimization methods were Akaike’s information criteria (AIC) and Minimum description length. Methods were evaluated with a publicly available dataset comprised of approximately 10-minute measurements from 39 healthy subjects at rest. From the evaluated AR model parameter extraction methods, Burg's method combined AIC performed the best. We obtained the mean absolute error of 2.7 and bias of -0.4 respirations per minute with this combination.

ASJC Scopus subject areas

Keywords

  • Autoregression, Particle filtering, Photoplethysmography, Respiration rate

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