Amplitude Suppression and Chaos Control in Epileptic EEG Signals

In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected during epileptic seizure. Then we have proposed a measure of chaoticity for a chaotic signal, which is somewhat similar to measuring sensitive dependence on initial conditions by measuring Lyapunov exponent in a chaotic dynamical system. We have shown that with respect to this measure the amplitude suppression algorithm reduces chaoticity in a chaotic signal (EEG signal is chaotic). We have compared our measure with the estimated largest Lyapunov exponent measure by the largelyap function, which is similar to Wolf's algorithm. They fit closely for all but one of the cases. How the algorithm can help to improve patient specific dosage titration during vagus nerve stimulation therapy has been outlined.