@inproceedings{9736eb0d8eb441ea805282087c5309bd,
title = "Presence of nonlinearity in intracranial EEG recordings: Detected by Lyapunov exponents",
abstract = "In this communication, we performed nonlinearity analysis in the EEG signals recorded from patients with temporal lobe epilepsy (TLE). The largest Lyapunov exponent (Lmax) and phase randomization surrogate data technique were employed to form the statistical test. EEG recordings were acquired invasively from three patients in six brain regions (left and right temporal depth, subtemporal and orbitofrontal) with 28-32 depth electrodes placed in depth and subdural of the brain. All three patients in this study have unilateral epileptic focus region on the right hippocampus(RH). Nonlinearity was detected by comparing the Lmax profiles of the EEG recordings to its surrogates. The nonlinearity was seen in all different states of the patient with the highest found in post-ictal state. Further our results for all patients exhibited higher degree of differences, quantified by paired t-test, in Lmax values between original and its surrogate from EEG signals recorded from epileptic focus regions. The results of this study demonstrated the Lmax is capable to capture spatio-temporal dynamics that may not be able to detect by linear measurements in the intracranial EEG recordings.",
keywords = "Epilepsy, Lyapunov exponents, Surrogate data",
author = "Liu, {Chang Chia} and Shiau, {Deng Shan} and Chaovalitwongse, {W. Art} and Pardalos, {Panos M.} and Sackellares, {J. C.}",
year = "2007",
month = dec,
day = "1",
doi = "10.1063/1.2817342",
language = "English (US)",
isbn = "9780735404670",
series = "AIP Conference Proceedings",
pages = "197--205",
booktitle = "Data Mining, Systems Analysis, and Optimization in Biomedicine",
note = "Conference on Data Mining, Systems Analysis, and Optimization in Biomedicine, 2007 ; Conference date: 28-03-2007 Through 30-03-2007",
}