Xiao, S, Yang, P, Liu, L et al. (2 more authors) (2020) Extraction of Respiratory Signals and Respiratory Rates from the Photoplethysmogram. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. EAI International Conference on Body Area Networks, 21 Oct 2020, Tallinn, Estonia. Springer Verlag , Cham, Switzerland , pp. 184-198. ISBN 9783030649906
Abstract
Respiration rate (RR) is an important indicator of human health assessment which can be estimated by extracting respiratory signals from the photoplethysmogram (PPG). The goal of this study is to propose an alternative method, for obtaining accurate estimation of respiratory rate (RR) from the PPG signal. The proposed algorithm is based on the multiple autoregressive models and autocorrelation analysis (AC-AR). In AC-AR, the autoregressive model (AR) is applied to determining the dominant respiratory rate from the PPG, and autocorrelation is applied to reduce the effect of clutter in the three respiratory-induced variations. Meanwhile, this paper introduced signal quality indices (SQI) to improve reliability of results. This algorithm is tested using an open source database: The CapnoBase benchmark dataset, which comprising 42 eight-minute PPG recording and respiratory signal acquired form both children and adults in different clinical setting. Compared with that of existing method in the literature, the average absolute error percentage (AAEP) of the proposed algorithm is less than 3.72%, which demonstrated that our presented AC-AR bring a significant improvement in accuracy.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020. This is an author produced version of a conference paper published in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Respiratory rate (RR); Photoplethysmography (PPG); AR model; Data fusion |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2021 11:26 |
Last Modified: | 09 Jan 2021 18:22 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-64991-3_13 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169602 |