Early detection of focal cerebral ischemia using bispectrum parameters of EEG in neural networks

This paper presents a new approach to evaluate the extent and locate the area of focal ischemic injury by using bispectrum estimation of electroencephalograms (EEGs) and 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 different blood streams, based on the model for rat focal ischemic cerebral injury described in this paper. Four channels of EEG were collected in each rat at the scheduled time of ischemia. The maximum magnitude and the weighted center of EEG bispectrum (WCOB) were extracted from the EEG bispectrum and a four layer ANN was employed for prediction. Training and testing the ANN used the ´leave one out´ method. The level and location of ischemic injury was 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 and simply locate the ischemic area in average accuracy of 91.67% and 100% of the cases, respectively. The results show that the method can be used for the detection of focal cerebral ischemia in clinical prognosis.