This method evaluates the signal regularity in the EEG series for detection of the BSP. A recent method based on the information theory and nonlinear time series analysis (approximate entropy) has been also developed. The bispectral method was designed to distinguish the BSP in the EEG series, but it is based on a two-dimensional function, which requires complicated computational processes. Although these methods can successfully obtain the frequency and spectral characteristics of the BSP, they ignore the intense nonlinearity of the BSP, resulting in low accuracy of detection.
Early methods were based on the spectral analysis, such as the spectral edge frequency and the median frequency. Many researchers have investigated methods for BSP detection. It is commonly used as a monitor for the titration of sedatives in order to achieve a maximum reduction of cerebral metabolic rate. So, the BSP can be seen as a defined “reference point” during administration of anesthetic or sedative agents and is considered a reliable indicator of adequate cerebral-protection for various neurosurgical diseases. Each series of successive bursts can be viewed as an attempted recovery of basal cortical dynamics. The BSP is a representative of the interaction between neuronal dynamics and brain metabolism. It can be observed in different clinical conditions (head trauma, stroke, coma, anoxia, and hypothermia) and can also be induced by pharmacological agents such as anesthetics, analgesics, or antiepileptic drugs. The electroencephalographic burst suppression pattern (BSP) consists of high amplitude bursts interrupted by low amplitude suppressions. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems. Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. 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%, ). Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Firstly we obtain the best selection of parameters for RP analysis. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. 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. Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia.