Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy

Abstract

Abstract:Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time-frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures' onset. These trends pointed to a slow decrease of the epileptic brain's global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.

Absence seizure detection classifying matching pursuit features of EEG signals

“…An efficient signal decomposition algorithm into time-frequency components (atoms) called Matching Pursuit (MP) [16]. MP has been used in EEG analysis [17][18][19] but there are only few studies related to seizure detection [20][21][22][23][24]. Franaszczuk et al [20], used the MP algorithm in order to provide the time-frequency decomposition of the EEG signals, searching for a definite change in ictal time-frequency pattern and revealing a predominant frequency of 5.3-8.4 Hz in the non-ictal interval.…”

“…The Multivariate Matching Pursuit (MMP) approach can also be estimated in which time-frequency atoms from all multichannel data are extracted [26]. In [24], Liu et al use MMP and the trends of Gabor entropic measures in order to predict an upcoming seizure.…”

“…Three more features suggested by Liu at al. in a 2018 study [24], were calculated for each time window: Gabor Energy (GEn) as the total energy of the selected atoms: Gabor Entropy (GE):…”