Research Article Detection of Burst Suppression Patterns in EEG Using Recurrence Rate Zhenhu Liang, 1 Yinghua Wang, 2,3 Yongshao Ren, 1 Duan Li, 4 Logan Voss, 5 Jamie Sleigh, 5 and Xiaoli Li 2,3 1 Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China 2 State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China 3 Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China 4 Institute of Information and Science Engineering, Yanshan University, Qinhuangdao 066004, China 5 Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand Correspondence should be addressed to Xiaoli Li; xiaoli@bnu.edu.cn 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. his 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. 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. his study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. he RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. hen, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. hen 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%,  = 0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. he purposed RR may provide an efective burst suppression detector for developing new patient monitoring systems. 1. Introduction he electroencephalographic burst suppression pattern (BSP) consists of high amplitude bursts interrupted by low ampli- tude suppressions. It can be observed in diferent clinical conditions (head trauma, stroke, coma, anoxia, and hypother- mia) [1, 2] and can also be induced by pharmacological agents such as anesthetics, analgesics, or antiepileptic drugs [3]. he BSP is a representative of the interaction between neuronal dynamics and brain metabolism. Each series of successive bursts can be viewed as an attempted recovery of basal cortical dynamics [4]. So, the BSP can be seen as a deined “reference point” during administration of anesthetic or sedative agents and is considered a reliable indicator of adequate cerebral-protection for various neurosurgical diseases. It is commonly used as a monitor for the titration of sedatives in order to achieve a maximum reduction of cerebral metabolic rate [5]. Many researchers have investigated methods for BSP detection. Early methods were based on the spectral analysis, such as the spectral edge frequency and the median frequency [6, 7]. Although these methods can successfully obtain the frequency and spectral characteristics of the BSP [8], they ignore the intense nonlinearity of the BSP, resulting in low accuracy of detection. he bispectral method was designed to distinguish the BSP in the EEG series, but it is based on a two-dimensional function, which requires complicated com- putational processes. A recent method based on the informa- tion theory and nonlinear time series analysis (approximate entropy) has been also developed [9]. his method evaluates Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 295070, 11 pages http://dx.doi.org/10.1155/2014/295070