The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

Silva, F.H. Lopes da

doi:10.1109/rbme.2008.2008246

Cited by 44 publications

(13 citation statements)

References 119 publications

(116 reference statements)

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“…Therefore, due to abnormal fluctuations of some variables (in the present simulations, abnormal increase of cortical input), the transition between normal ongoing and the seizure activities can occur. This concept is in agreement with the general theoretical framework of the dynamics of the brain activity which considers epilepsy as a dynamical disease of brain system [39,40]. To illustrates the transition between normal and seizure attractors, the phase-planes of model outputs for the time interval 45-75 s (top panel) and 95-125 s (bottom panel) for healthy and pathological astrocytes are shown in Fig.…”

Section: Simulation Results and Discussionsupporting

confidence: 82%

Functional modeling of astrocytes in epilepsy: a feedback system perspective

Amiri

Bahrami

Janahmadi

2010

Neural Comput & Applic

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Astrocytes, a subtype of glial cells, in the brain provide structural and metabolic supports to the nervous system. They are also active partners in synaptic transmission and neuronal activities. In the present study, a biologically plausible thalamocortical neural population model (TCM) originally proposed by Suffczynski et al. (Neuroscience 126(2):467-484, 2004) is extended by integrating the functional role of astrocytes in the regulation of synaptic transmission. Therefore, the original TCM is modified to consider neuron-astrocyte interactions. Using the modified model, it is demonstrated that the healthy astrocytes are capable to compensate the variation of cortical excitatory input by increasing their firing frequency. In this way, they can preserve the attractor corresponding to the normal activity. Furthermore, the performance of the pathological astrocytes is also investigated. It is hypothesized that one of the plausible causes of seizures is the malfunction of astrocytes in the regulatory feedback loop. That is, pathologic astrocytes are not any more able to regulate and/or compensate the excessive increase of the cortical input. Therefore, pathologic astrocytes lead to the emergence of paroxysmal attractor.Results demonstrate that disruption of the homeostatic or signaling function of astrocytes can initiate the synchronous firing of neurons, suggesting that astrocytes might be one of the potential targets for the treatment of epilepsy.

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Section: Simulation Results and Discussionsupporting

confidence: 82%

Functional modeling of astrocytes in epilepsy: a feedback system perspective

Amiri

Bahrami

Janahmadi

2010

Neural Comput & Applic

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“…The PA is defined in [41] as: 13) where Ns is the number of correctly predicted seizures and Na is the total number of seizures.…”

Section: Resultsmentioning

confidence: 99%

“…This disease can be controlled by medication in 70% of cases [7]. Epilepsy can be monitored by Electroencephalogram (EEG) [8]- [13], by following brain activities and discovering electrochemical disturbances in the neurons through the output of the electrodes [14].…”

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confidence: 99%

Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals Using Phase Correlation

Parvez

Paul

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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Automated seizure prediction has a potential in epilepsy monitoring, diagnosis, and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and prediction. This paper proposes a new seizure prediction approach based on spatiotemporal relationship of EEG signals using phase correlation. This measures the relative change between current and reference vectors of EEG signals which can be used to identify preictal/ictal (before the actual seizure onset/ actual seizure period) and interictal (period between adjacent seizures) EEG signals to predict the seizure. The experiments show that the proposed method is less sensitive to artifacts and provides higher prediction accuracy (i.e., 91.95%) and lower number of false alarms compared to the state-of-the-art methods using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set.

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“…they require prior assumptions on what to find (Mosher et al, 1993;Lantz et al, 1996;Merlet and Gotman, 1999;Fuchs et al, 2004;Lopes da Silva, 2008).…”

Section: Source Localizationmentioning

confidence: 99%