On the discrimination of patho-physiological states in epilepsy by means of dynamical measures

• Published in: Computers in biology and medicine 2009-12, Vol.39 (12), p.1073-1082
• Main Authors: Raiesdana, Somayeh, Golpayegani, Seyed Mohammad Reza Hashemi, Firoozabadi, Seyed Mohammad P, Mehvari Habibabadi, Jafar
• Format: Article
• Language: English
• Subjects: Adult; Brain - physiopathology; Complexity; Computational Biology; Control algorithms; Databases, Factual; Diagnosis, Computer-Assisted - methods; Diagnosis, Computer-Assisted - statistics & numerical data; Discrimination; Electroencephalography - statistics & numerical data; Epilepsy; Epilepsy - diagnosis; Epilepsy - etiology; Epilepsy - physiopathology; Fuzzy Logic; Humans; Internal Medicine; Methods; Models, Neurological; Neural networks; Neurons; Nonlinear dynamic; Nonlinear Dynamics; Other; Phase transition; Phase transitions; Recurrence; Route to seizure; Seizure control; Studies; Temporal lobe epilepsy
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2009.09.001

Abstract 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.