Next Article in Journal
LSTM-CRF for Drug-Named Entity Recognition
Next Article in Special Issue
Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home
Previous Article in Journal
Entropy Generation Rates through the Dissipation of Ordered Regions in Helium Boundary-Layer Flows
Previous Article in Special Issue
Entropy Information of Cardiorespiratory Dynamics in Neonates during Sleep
Open AccessFeature PaperArticle

Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing

1
Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics & Computer Science, University of Twente, 7500 AE Enschede, The Netherlands
2
Department of Electrical & Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
3
Department of Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(6), 282; https://doi.org/10.3390/e19060282
Received: 6 May 2017 / Revised: 7 June 2017 / Accepted: 9 June 2017 / Published: 16 June 2017
(This article belongs to the Special Issue Entropy and Sleep Disorders)
Pulse rate variability (PRV), an alternative measure of heart rate variability (HRV), is altered during obstructive sleep apnea. Correntropy spectral density (CSD) is a novel spectral analysis that includes nonlinear information. We recruited 160 children and recorded SpO2 and photoplethysmography (PPG), alongside standard polysomnography. PPG signals were divided into 1-min epochs and apnea/hypoapnea (A/H) epochs labeled. CSD was applied to the pulse-to-pulse interval time series (PPIs) and five features extracted: the total spectral power (TP: 0.01–0.6 Hz), the power in the very low frequency band (VLF: 0.01–0.04 Hz), the normalized power in the low and high frequency bands (LFn: 0.04–0.15 Hz, HFn: 0.15–0.6 Hz), and the LF/HF ratio. Nonlinearity was assessed with the surrogate data technique. Multivariate logistic regression models were developed for CSD and power spectral density (PSD) analysis to detect epochs with A/H events. The CSD-based features and model identified epochs with and without A/H events more accurately relative to PSD-based analysis (area under the curve (AUC) 0.72 vs. 0.67) due to the nonlinearity of the data. In conclusion, CSD-based PRV analysis provided enhanced performance in detecting A/H epochs, however, a combination with overnight SpO2 analysis is suggested for optimal results. View Full-Text
Keywords: pulse rate variability; correntropy; smartphone-based pulse oximetry pulse rate variability; correntropy; smartphone-based pulse oximetry
Show Figures

Figure 1

MDPI and ACS Style

Garde, A.; Dehkordi, P.; Ansermino, J.M.; Dumont, G.A. Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing. Entropy 2017, 19, 282.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop