On the discrimination of patho-physiological states in epilepsy by means of dynamical measures.
In the present paper a number of techniques were applied to determine the effects of epileptic seizure on spontaneous ongoing EEG. The idea is that seizure represents transitions of an epileptic brain from its normal (chaotic) state to an abnormal (more ordered) state. Some nonlinear measures including correlation dimension, maximum Lyapunov exponent and wavelet entropy and a graphical tool, named recurrence plot, as well as a novel technique that collects some statistics of the state space organization were used to characterize interictal, preictal and ictal states and derivate a phase transition. The novelty of this work includes of introducing new types of indicators base upon some nonlinear features besides of proposing a new feature of point distribution in phase space. Our results show that (1) these three states are separable in 3-D feature space of nonlinear measures with a gradual decrease of their quantity in seizure evolution, (2) strong rhythmicity, which manifests in recurrence plots and recurrence quantification analysis measures, appears in dynamic while having entered into seizure and (3) different volumes of state space are occupied during each phase of epileptic disorder. The significance of the work is that this information is a step into the detection of a preictal state and consequently is helpful in the prediction and control of epileptic seizures.
Raiesdana
Somayeh
S
Islamic Azad University, Tehran, Iran.
Golpayegani
Seyed Mohammad Reza Hashemi
SM
Firoozabadi
Seyed Mohammad P
SM
Mehvari Habibabadi
Jafar
J
D000328:Adult; D001921:Brain / Q000503:physiopathology; D019295:Computational Biology; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology*; D017143:Fuzzy Logic; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D012008:Recurrence