Characterization of the temporal pattern of cerebral blood flow oscillations

Oscillation of the cerebral blood flow (CBF) is a common feature in several physiological or pathophysiological states of the brain. It is a promising opportunity to identify the state of the brain based on the classification of CBF signals. In order to carry out classification of the time signals, a feature vector has been extracted to characterize the signals. Unsupervised classification showed that the extracted feature vector is an acceptable representation of the time signals. It also turned out that the difference between normal signal and a signal indicating drug injection effect is significant, and much more dominant than the difference between signals of the right and left brain sides. For the signal classification an artificial neural network (ANN) model based on supervised backpropagation network has been developed and successfully applied.