title: Detection of burst suppression patterns in EEG using recurrence rate.
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%, 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.
authors.affiliation: Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
authors.forename: Zhenhu
authors.identifier: 0000-0002-2467-9877
authors.initials: Z
authors.lastname: Liang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Yinghua
authors.identifier: 0000-0002-7751-1672
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
authors.forename: Yongshao
authors.initials: Y
authors.lastname: Ren
authors.affiliation: Institute of Information and Science Engineering, Yanshan University, Qinhuangdao 066004, China.
authors.forename: Duan
authors.identifier: 0000-0001-9102-0741
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand.
authors.forename: Logan
authors.identifier: 0000-0002-2938-6045
authors.initials: L
authors.lastname: Voss
authors.affiliation: Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand.
authors.forename: Jamie
authors.initials: J
authors.lastname: Sleigh
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Li
mesh_terms: D000293:Adolescent; D000328:Adult; D000768:Anesthesia, General / Q000009:adverse effects; D001921:Brain / Q000503:physiopathology*; D001927:Brain Diseases / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult