Surrogate data test for nonlinearity of EEG signals: a newborn EEG burst suppression case study

Mirzaei, Parisa, Azemi, Ghasem, Japaridze, Natia and Boashash, Boualem (2017) Surrogate data test for nonlinearity of EEG signals: a newborn EEG burst suppression case study. Digital Signal Processing, 70 30-38. doi:10.1016/j.dsp.2017.07.010


Author Mirzaei, Parisa
Azemi, Ghasem
Japaridze, Natia
Boashash, Boualem
Title Surrogate data test for nonlinearity of EEG signals: a newborn EEG burst suppression case study
Journal name Digital Signal Processing   Check publisher's open access policy
ISSN 1051-2004
1095-4333
Publication date 2017-11-01
Year available 2017
Sub-type Article (original research)
DOI 10.1016/j.dsp.2017.07.010
Open Access Status Not yet assessed
Volume 70
Start page 30
End page 38
Total pages 9
Place of publication Waltham, MA, United States
Publisher Academic Press
Language eng
Abstract This paper applies the surrogate data method to investigate the presence of nonlinearity in neonatal electroencephalogram (EEG) burst suppression (B/S) patterns in order to rationalize the use of nonlinear methods for automated detection of such patterns. To generate surrogate data, the statically transformed autoregressive process (STAP) algorithm is deployed, and, the correlation dimension (CD) and asymmetry due to time reversal (REV) are applied as discriminating statistics. The results of the surrogate data test demonstrate the nonlinearity characteristic of real neonatal EEG signals during both burst and suppression phases at the 0.05 significance level. The evidence of nonlinearity is found in 90% and 87% of bursts and suppressions respectively. Furthermore, the ability of nonlinear tools in detecting B/S patterns in multichannel neonatal EEG signals is investigated using receiver operating characteristic analysis. The experimental results show that the CD outperforms existing methods based on the nonlinear energy operator. (C) 2017 Elsevier Inc. All rights reserved.
Keyword Automated detection
Burst suppression
Nonlinear features
Nonlinearity in newborn EEGs
Surrogate data method
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 6-885-2-364 | NPRP 4-1303-2-517
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
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