Discrimination of cerebral ischemic states using bispectrum analysis of EEG and artificial neural network

No doubt a noninvasive technique for detection of cerebral ischemic extent, before the formation of the focus, is extremely valuable. This paper presents a new approach to early evaluate the degree of ischemic injury by combining bispectrum estimation of electroencephalograms (EEGs) with artificial neural network (ANN). The graded ischemic injuries in 24 Sprague-Dawley (SD) rats were induced for different periods of 8, 18, 30 min by infusing physiological saline along the left blood stream, based on the model for rat ischemic cerebral injury described in this paper. Four channels of EEG were collected in each rat at scheduled time of ischemia. The maximum bicoherence index and the weighted center of bispectrum (WCOB) were extracted from the EEGs and were used as input feature vector of a four-layer (12-7-2-1) ANN for prediction. Training and testing the ANN used the 'leave one out' strategy. The levels of ischemic injury were verified and classified by observing the ischemic area by conventional hematoxylin and eosin (HE) staining and the heat shock protein (HSP70) test. The proposed method was able to correctly detect ischemic extent in average accuracy of 91.67% of the cases. The results show that this scheme can be expected to diagnose ischemic cerebral injury in its earlier phases.