Evaluation of ischemic states using bispectrum parameters of EEG and neural networks

No doubt a noninvasive technique for detection of focal cerebral ischemic extent, before the focus is formed, is extremely valuable. This work presents a new approach to early evaluate the degree of focal 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. Four channels of EEG were collected in each rat at the scheduled time of ischemia. The maximum bicoherence index and the weighted center of EEG bispectrum (WCOB) were extracted from the EEG bispectrum and were used as the 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 in the heat shock protein (HSP70) test. The proposed system was able to correctly detect the ischemic extent in average accuracy of 91.67% of the cases. The results show that the scheme can be expected to diagnose ischemic cerebral injury in its earlier phases.