Its Possible Use for Interictal Focus Localization, Seizure Anticipation, and Prevention

Lehnertz, Klaus; Andrzejak, Ralph G.; Arnhold, J.; Kreuz, Thomas; Mormann, Florian; Rieke, Christoph; Widman, Guido; Elger, Christian E.

doi:10.1097/00004691-200105000-00002

Cited by 170 publications

(88 citation statements)

References 75 publications

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“…We have analyzed a total of 24 hours of ECoG recordings (mean 8 hours, range 200 minutes-15 hours 30 minutes), containing a total of 12 seizures (mean 4, range [3][4][5]. The data were recorded with a sampling frequency of 400 Hz and band-pass filtered offline to 0.5-50 Hz using moving overlapping windows and a finite impulse response filter with a Kaiser window.…”

Section: Methods a Patients And Ecog Recordingsmentioning

confidence: 99%

Detecting and localizing the foci in human epileptic seizures

Ben‐Jacob

Boccaletti

Pomyalov

et al. 2007

Chaos

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We consider the electrical signals recorded from a subdural array of electrodes placed on the pial surface of the brain for chronic evaluation of epileptic patients before surgical resection. A simple and computationally fast method to analyze the interictal phase synchrony between such electrodes is introduced and developed with the aim of detecting and localizing the foci of the epileptic seizures. We evaluate the method by comparing the results of surgery to the localization predicted here. We find an indication of good correspondence between the success or failure in the surgery and the agreement between our identification and the regions actually operated on.Epilepsy is defined as a condition of chronic unprovoked seizures. It is one of the most common neurologic disorders, affecting more than 50 million people worldwide. About 1/3 of cases are not adequately controlled by medication, causing a severe reduction in independence and quality of life. If an individual's seizures are stereotyped and appear to have a focal onset, surgical resection may be a consideration. Localizing accurately the epileptic focus (or foci) is therefore essential part of surgical planning. We propose a simple and computationally fast method based on phase synchronization, that is able to correctly localize the foci of epileptic seizures. The method was applied to the data of three patients. The overlap of the location of the electrodes identified by our method and the resected areas was consistent with the degree of surgery success.

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Section: Methods a Patients And Ecog Recordingsmentioning

confidence: 99%

Detecting and localizing the foci in human epileptic seizures

Ben‐Jacob

Boccaletti

Pomyalov

et al. 2007

Chaos

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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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“…However, a robust definition of such a state and with it, its possibility to predict an impeding seizure, is a much harder mathematical problem. For the time being, one can assume the existence of a preictal state based on recent investigations that have found physiological and clinical support for the idea that certain types of seizures are predictable, [44,45,46,47,48,49]. Functional mri based evidence of transitional states preceding seizures [8] and interictal spikes [50] can also lend support to this hypothesis.…”

Section: A1 Epilepsy and Eegmentioning

confidence: 99%

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Dunn¹,

Abbott²,

Masterton³

et al. 2011

ANZIAMJ

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The Brain Research Institute (bri) uses various types of indirect measurements, including eeg and fmri, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the bri asked the misg 2010 participants to examine how the available eeg and fmri data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was

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Its Possible Use for Interictal Focus Localization, Seizure Anticipation, and Prevention

Cited by 170 publications

References 75 publications

Detecting and localizing the foci in human epileptic seizures

Detecting and localizing the foci in human epileptic seizures

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

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

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