Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107619

Title:	Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms
Authors:	李柏磊;Yeh, Jia-Rong;Peng, Chung-Kang;Lo, Men-Tzung;Yeh, Chien-Hung;Chen, Shih-Ching;Wang, Cheng-Yen;Lee, Po-Lei;Kang, Jiunn-Horng
Contributors:	資訊電機學院電機工程學系
Keywords:	Algorithms;Autonomic Nervous System - physiology;Brain - physiology;Detrended fluctuation analysis;Electroencephalography;Female;Heart Rate - physiology;Heart rate variability;Hilbert Huang transform;Humans;Nonlinear Dynamics;Polysomnography;Signal Processing, Computer-Assisted;Sleep EEG;Sleep Stages - physiology;Sympathovagal modulation
Date:	2013-10-15
Issue Date:	2026-04-23 14:19:24 (UTC+8)
Publisher:	Elsevier;Netherlands: Elsevier B.V
Abstract:	摘要： •A new model for monitoring the sleep stages is built based on Hilbert Huang transform.•Two main oscillations were defined to depict the feature of sleep EEG based on HHT.•Slow- and fast-waves oscillations correspond to fluctuations in the delta and high frequency band.•DFA α1 was used to reflect the ANS activity during sleep.•The relationship between sleep EEG and HRV was significantly confirmed in this study. The multi-mode modulation is a key feature of sleep EEG. And the short-term fractal property reflects the sympathovagal modulation of heart rate variability (HRV). The properties of EEG and HRV strongly correlated with sleep status and are interesting in clinic diagnosis. 19 healthy female subjects were included for over-night standard polysomnographic study. Hilbert Huang transform (HHT) was used to characterize the temporal features of slow- and fast-wave oscillations decomposed from sleep EEG at different stages. Masking signals were used for solving the mode-mixing problem in HHT. On the other hand, detrended fluctuation analysis (DFA) was used to assess short-term property of HRV denoted as DFA α1, which reflects the temporal activity of autonomic nerve system (ANS). Thus, the dynamic interaction between sleep EEG and HRV can be examined through the relationship between the features of sleep EEG and DFA α1 of HRV. The frequency feature of sleep EEG serves as a good indicator for the depth of sleep during non-rapid eye movement (NREM) sleep, and amplitude feature of fast-wave oscillation is a good index for distinguishing rapid eye movement (REM) from NREM sleep. The relationship between DFA α1 of HRV and the mean amplitude of fast-wave oscillation of sleep EEG affirmed with Pearson correlation coefficient is more significant than the correlation verified by the traditional spectral analysis. The dynamic properties of sleep EEG and HRV derived by EMD and DFA represent important features for cortex and ANS activities during sleep. 其他題名： J Neurosci Methods 出版者： Netherlands: Elsevier B.V 出版日期： 2013-10-15 出處： Journal of Neuroscience Methods, 2013-10, Vol.219 (2), p.233-239 版權： 2013 Elsevier B.V. 版權： Copyright © 2013 Elsevier B.V. All rights reserved. 識別號： ISSN: 0165-0270 識別號： ISSN: 1872-678X 識別號： EISSN: 1872-678X 識別號： DOI: 10.1016/j.jneumeth.2013.08.008 識別號： PMID: 23965234
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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