Long-term prospective on-line real-time seizure prediction

Iasemidis, Leonidas D.; Shiau, Deng‐Shan; Pardalos, Pãnos M.; Chaovalitwongse, Wanpracha Art; Narayanan, K.; Prasad, Awadhesh; Tsakalis, Konstantinos; Carney, Paul R.; Sackellares, J. Chris

doi:10.1016/j.clinph.2004.10.013

Cited by 190 publications

(83 citation statements)

References 39 publications

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“…It is tempting to speculate that preictal unit activity might be related to prodromal symptoms some patients consistently experience several minutes before seizure onset (Rajna et al 1997). Quantitative analyses of EEG recordings detect significant preictal changes in patients with temporal lobe epilepsy (Iasemidis et al 2005;Le Van Quyen et al 2000Lehnertz and Elger 1998;Litt et al 2001;Martinerie et al 1998;Navarro et al 2005). The activity of preictalincrease cells may be related to some of these preictal EEG changes.…”

Section: Preictal-increase Patternmentioning

confidence: 99%

Changes in Granule Cell Firing Rates Precede Locally Recorded Spontaneous Seizures by Minutes in an Animal Model of Temporal Lobe Epilepsy

Bower¹,

Buckmaster²

2008

Journal of Neurophysiology

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Bower MR, Buckmaster PS. Changes in granule cell firing rates precede locally recorded spontaneous seizures by minutes in an animal model of temporal lobe epilepsy. J Neurophysiol 99: 2431-2442, 2008. First published March 5, 2008 doi:10.1152/jn.01369.2007. Although much is known about persistent molecular, cellular, and circuit changes associated with temporal lobe epilepsy, mechanisms of seizure onset remain unclear. The dentate gyrus displays many persistent epilepsyrelated abnormalities and is in the mesial temporal lobe where seizures initiate in patients. However, little is known about seizurerelated activity of individual neurons in the dentate gyrus. We used tetrodes to record action potentials of multiple, single granule cells before and during spontaneous seizures in epileptic pilocarpinetreated rats. Subsets of granule cells displayed four distinct activity patterns: increased firing before seizure onset, decreased firing before seizure onset, increased firing only after seizure onset, and unchanged firing rates despite electrographic seizure activity in the immediate vicinity. No cells decreased firing rate immediately after seizure onset. During baseline periods between seizures, action potential waveforms and firing rates were similar among the four subsets of granule cells in epileptic rats and in granule cells of control rats. The mean normalized firing rate of granule cells whose firing rates increased before seizure onset deviated from baseline earliest, beginning 4 min before dentate gyrus electrographic seizure onset, and increased progressively, more than doubling by seizure onset. It is generally assumed that neuronal firing rates increase abruptly and synchronously only when electrographic seizures begin. However, these findings show heterogeneous and gradually building changes in activity of individual granule cells minutes before spontaneous seizures.

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Section: Preictal-increase Patternmentioning

confidence: 99%

Changes in Granule Cell Firing Rates Precede Locally Recorded Spontaneous Seizures by Minutes in an Animal Model of Temporal Lobe Epilepsy

Bower¹,

Buckmaster²

2008

Journal of Neurophysiology

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“…However, there is increasing clinical and neurophysiological evidence that the change may be more gradual, involving a pre-ictal transition state, which then leads to a seizure [5,17,23]. The transition from post-ictal to inter-ictal may be gradual as well; EEG abnormalities such as focal or diffuse polymorphic delta activity, attenuation or activation or focal spikes have been demonstrated upto several hours after a seizure [24].…”

Section: Neurobiological Mechanisms Underlying the Peri-ictal And Ictmentioning

confidence: 99%

Peri-Ictal and Ictal Cognitive Dysfunction in Epilepsy

Vijayaraghavan

Natarajan

Krishnamoorthy

2011

Behavioural Neurology

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Abstract. Disturbances in cognitive function, particularly memory, are a common complaint of patients with epilepsy. Factors contributing to cognitive dysfunction are the type of epilepsy, type and frequency of seizures, anti-epileptic drugs and the location of underlying brain lesions. Whilst a great deal of attention has been paid to permanent cognitive impairment, the nature and underlying mechanisms of ictal and peri-ictal cognitive changes are poorly understood. In-depth investigation of seizure related cognitive dysfunction is of great clinical relevance, as these changes are potentially reversible and treatable, thus reducing the cumulative effect of frequent seizures Greater knowledge of peri-ictal and ictal cognitive dysfunction would improve seizure prediction, localization of seizure focus and assessment of treatment effectiveness, greatly reducing distress and disability. This paper will review current understanding of peri-ictal and ictal cognitive dysfunction and discuss future directions for research.

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“…Only electrodes near epileptogenic focus shows apparent changes when seizures are coming [1]. These electrodes are obtained knowledge through clinical diagnosis, shown in Table 1.…”

Section: Seizure Prediction Algorithmmentioning

confidence: 99%

An Epileptic Seizure Prediction Algorithm from Scalp EEG Based on Morphological Filter and Kolmogorov Complexity

Wang

Zhang

2007

Digital Human Modeling

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Epilepsy is the most common neurological disorder in the world, second only to stroke. There are nearly 15 million patients suffer from refractory epilepsy, with no available therapy. Although most seizures are not life threatening, they are an unpredictable source of annoyance and embarrassment, which will result in unconfident and fear. Prediction of epileptic seizures has a profound effect in understanding the mechanism of seizure, improving the rehabilitation possibilities and thereby the quality of life for epilepsy patients. A seizure prediction system can help refractory patients rehabilitate psychologically.In this paper, we introduce an epilepsy seizure prediction algorithm from scalp EEG based on morphological filter and Kolmogorov complexity. Firstly, a complex filter is constructed to remove the artifacts in scalp EEG, in which a morphological filter with optimized structure elements is proposed to eliminate the ocular artifact. Then, the improved Kolmogorov complexity is applied to describe the non-linear dynamic transition of brains. Results show that only the Kolmogorov complexity of electrodes near the epileptogenic focus reduces significantly before seizures. Through the analysis of 7 long-term scalp EEG recordings from 5 epilepsy patients, the average prediction time is 8.5 minutes, the mean sensitivity is 74.0% and specificity is 33.6%.

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Long-term prospective on-line real-time seizure prediction

Cited by 190 publications

References 39 publications

Changes in Granule Cell Firing Rates Precede Locally Recorded Spontaneous Seizures by Minutes in an Animal Model of Temporal Lobe Epilepsy

Changes in Granule Cell Firing Rates Precede Locally Recorded Spontaneous Seizures by Minutes in an Animal Model of Temporal Lobe Epilepsy

Peri-Ictal and Ictal Cognitive Dysfunction in Epilepsy

An Epileptic Seizure Prediction Algorithm from Scalp EEG Based on Morphological Filter and Kolmogorov Complexity

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