Artifacts classification and apnea events detection in neck photoplethysmography signals
File(s)
Published version
Author(s)
Garcia-Lopez, Irene
Pramono, Renard Xaviero Adhi
Rodriguez-Villegas, Esther
Type
Journal Article
Abstract
The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP).
Date Issued
2022-12
Date Acceptance
2022-09-12
Citation
Medical and Biological Engineering and Computing, 2022, 60 (12), pp.3539-3554
ISSN
0140-0118
Publisher
Springer
Start Page
3539
End Page
3554
Journal / Book Title
Medical and Biological Engineering and Computing
Volume
60
Issue
12
Copyright Statement
© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000868469900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
ALGORITHM
Apnea detection
Computer Science
Computer Science, Interdisciplinary Applications
Engineering
Engineering, Biomedical
Life Sciences & Biomedicine
Mathematical & Computational Biology
Medical Informatics
MOTION ARTIFACT
Noise artifacts classification
Photoplethysmography (PPG)
Pulse oximetry
PULSE OXIMETRY
QUALITY ASSESSMENT
REDUCTION
Science & Technology
Sudden unexpected death in epilepsy (SUDEP)
Technology
TIME
Publication Status
Published
Date Publish Online
2022-10-17