Brain chirps: spectrographic signatures of epileptic seizures

Schiff, Steven J.; Colella, David; Jacyna, Gary M; Hughes, E.S.; Creekmore, J.; Marshall, Angela; Bozek-Kuzmicki, Maribeth; Benke, George; Gaillard, William D.; Conry, Joan A.; Weinstein, Steven R.

doi:10.1016/s1388-2457(00)00259-5

Cited by 130 publications

(83 citation statements)

References 16 publications

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“…Epileptic seizures manifest as uncontrolled limb movements, loss of consciousness and posture, and high-amplitude electroencephalographic (EEG) signals with complex dynamics (Dikanev et al 2005;Jirsch et al 2006;Schiff et al 2000;Worrell et al 2004). How synchronized activation of large neuronal populations-reflected by the large-amplitude EEG signal of seizures-is brought about is a question of high theoretical and clinical importance.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of GABAergic Inputs to CA3 Pyramidal Cells Precedes Seizure-Like Event Onset in Juvenile Rat Hippocampal Slices

Lasztóczi

Nyitrai

Héja

et al. 2009

Journal of Neurophysiology

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Lasztóczi B, Nyitrai G, Héja L, Kardos J. Synchronization of GABAergic inputs to CA3 pyramidal cells precedes seizure-like event onset in juvenile rat hippocampal slices. J Neurophysiol 102: 2538 -2553, 2009. First published August 12, 2009 doi:10.1152/jn.91318.2008. Here we address how dynamics of glutamatergic and GABAergic synaptic input to CA3 pyramidal cells contribute to spontaneous emergence and evolution of recurrent seizure-like events (SLEs) in juvenile (P10-13) rat hippocampal slices bathed in low- [Mg 2ϩ ] artificial cerebrospinal fluid. In field potential recordings from the CA3 pyramidal layer, a short epoch of high-frequency oscillation (HFO; 400 -800 Hz) was observed during the first 10 ms of SLE onset. GABAergic synaptic input currents to CA3 pyramidal cells were synchronized and coincided with HFO, whereas the glutamatergic input lagged by ϳ10 ms. If the intracellular [Cl Ϫ ] remained unperturbed (cell-attached recordings) or was set high with whole cell electrode solution, CA3 pyramidal cell firing peaked with HFO and GABAergic input. By contrast, with low intracellular [Cl Ϫ ], spikes of CA3 pyramidal cells lagged behind HFO and GABAergic input. This temporal arrangement of HFO, synaptic input sequence, synchrony of GABAergic currents, and pyramidal cell firing emerged gradually with preictal discharges until the SLE onset. Blockade of GABA A receptormediated currents by picrotoxin reduced the inter-SLE interval and the number of preictal discharges and did not block recurrent SLEs. Our data suggest that dynamic changes of the functional properties of GABAergic input contribute to ictogenesis and GABAergic and glutamatergic inputs are both excitatory at the instant of SLE onset. At the SLE onset GABAergic input contributes to synchronization and recruitment of pyramidal cells. We conjecture that this network state is reached by an activity-dependent shift in GABA reversal potential during the preictal phase.

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Section: Introductionmentioning

confidence: 99%

Synchronization of GABAergic Inputs to CA3 Pyramidal Cells Precedes Seizure-Like Event Onset in Juvenile Rat Hippocampal Slices

Lasztóczi

Nyitrai

Héja

et al. 2009

Journal of Neurophysiology

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“…Moreover, neurologists and medical staff could further perform behavioural testing to further assess which function may be impaired because of epileptic seizure and help them localize the source of epileptic seizure activity. Various methods have been used to predict and detect epileptic seizure activity, including frequencybased methods (46), time-domain analysis (47), intelligent approaches (48), nonlinear dynamics and chaos (49), and methods of delays (50). Additionally, to analyse the epileptic EEG recordings, several linear (51) and nonlinear methods (52) as well as multi-way array models (53) have been used to localize the seizure origin and further understand the complex structure of an epileptic seizure.…”

Section: Discussionmentioning

confidence: 99%

Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques

et al. 2018

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Objectives: The most common chronic disorder due to sudden change in the electrical activity of the brain is known as epilepsy. It causes millions of deaths every year and is the second major disorder after stroke. The epileptic process involves an abnormal synchronized firing of neurons usually characterized by recurrent seizures, which are highly complex, nonlinear and non-stationary in nature. Even between seizures, the epileptic brain is different from normal and pathological conditions. The classical methods fail to analyse the full dynamics in detecting epileptic seizure. The aim of this research was to quantify the dynamics of EEG signals between seizures and seizure-free intervals using entropy based complexity measures at multiple temporal scales. The complexity of epileptic seizure intervals is reduced because of degradation of structural and functional components. Thus, complexity measures are more robust to fully analyse the dynamics of these signals. Methods:The publicly available data comprise of three different groups of EEG signals: 1, Healthy subjects; 2, Epileptic subjects during seizure-free intervals (interictal EEG); and 3, Epileptic subjects during seizure (ictal EEG); each of 100 EEG channel sample at 174 Hz were taken to quantify the dynamics in these signals. To analyse the improved understanding of the epileptic process, complexity-based techniques of Multiscale Sample Entropy (MSE) and Wavelet Entropy (Wentropy) including Shannon, log energy, and threshold, and sure and norm developed in Matlab 2015a, were employed to distinguish these conditions. Mann-WhitneyWilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. Moreover, the area under the curve (AUC) was computed by developing multi-receiver operating curve (ROC) in Matlab 2015a to find the maximum separation to distinguish these conditions. Results: The complexity of healthy and epileptic subjects (including both in the presence of seizures and without seizure) was computed using MSE and Wentropy at multiple temporal scales. The healthy subjects exhibited higher complexity than the epileptic subjects. Likewise, the complexity of ictal (seizure subjects) was higher than the interictal (without seizures). To distinguish healthy subjects (Set O) from epileptic (Set S) subjects, the highest separation was obtained using wavelet norm 1.1 (AUC = 0.999) followed by wavelet Shannon (AUC = 0.9944), MSE (AUC = 0.9727) and wavelet threshold (AUC = 0.942). Conclusions: Optimal results using MSE were obtained at smaller scales whereas the wavelet entropies gave optimal results mostly at higher temporal scales. Moreover, the highest separation in form of AUC was obtained using the Wentropy method with norm parameter 1.1 to distinguish healthy (eye open) and epileptic seizure (ictal state) subjects followed by wavelet Shannon and MSE.

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“…Additionally, these methods can alert medical staff, and allow them to perform behavioural testing to further assess which specific functions may be impaired because of an epileptic seizure and help them in localizing the source of the epileptic seizure activity. Methods used to predict epileptic seizures include time-domain analysis (Lange, Lieb, Engel, & Crandall, 1983), frequency-based methods (Schiff et al, 2000), nonlinear dynamics and chaos , methods of delays (Le Van Quyen et al, 2001), and intelligent approaches (Geva & Kerem, 1998). Advances in seizure prediction promise to give rise to implantable devices able to warn of impending seizures and to trigger therapy to prevent clinical epileptic attacks (Litt & Echauz, 2002;McSharry, Smith, & Tarassenko, 2003).…”

Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%