Detection of Burst Suppression Patterns in EEG Using Recurrence Rate
Joint Authors
Sleigh, Jamie W.
Liang, Zhenhu
Ren, Yongshao
Li, Xiao-Li
Voss, Logan J.
Wang, Yinghua
Li, Duan
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-17
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia.
It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases.
This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG.
The RP analysis is applied to EEG data containing BSPs collected from 14 patients.
Firstly we obtain the best selection of parameters for RP analysis.
Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated.
Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests.
Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO).
ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P = 0.03 ).
Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients.
The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.
American Psychological Association (APA)
Liang, Zhenhu& Wang, Yinghua& Ren, Yongshao& Li, Duan& Voss, Logan J.& Sleigh, Jamie W.…[et al.]. 2014. Detection of Burst Suppression Patterns in EEG Using Recurrence Rate. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049114
Modern Language Association (MLA)
Liang, Zhenhu…[et al.]. Detection of Burst Suppression Patterns in EEG Using Recurrence Rate. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1049114
American Medical Association (AMA)
Liang, Zhenhu& Wang, Yinghua& Ren, Yongshao& Li, Duan& Voss, Logan J.& Sleigh, Jamie W.…[et al.]. Detection of Burst Suppression Patterns in EEG Using Recurrence Rate. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049114
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049114