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The Scientific World Journal
Volume 2014 (2014), Article ID 295070, 11 pages
http://dx.doi.org/10.1155/2014/295070
Research Article

Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

1Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
3Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
4Institute of Information and Science Engineering, Yanshan University, Qinhuangdao 066004, China
5Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand

Received 21 January 2014; Accepted 20 February 2014; Published 17 April 2014

Academic Editors: H.-K. Lam, J. Li, G. Ouyang, and T. Stathaki

Copyright © 2014 Zhenhu Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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%, ). 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.