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Time series analysis of the EEG signals for Epilepsy seizure forecast
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1  School of Science, Technology and Engineering Management, St. Thomas University, Miami Gardens, FL 33054, USA


Epilepsy is a Central Nervous Disorder that is affecting millions of people with a different degree of severity. Depending on the level the patient is at the moment, seizures can be controlled either with medications or with surgical procedures. However, during surgical procedures many complications might show up, mainly due to the lack of knowledge of functional neuronal networks operating behind epileptic seizures. Therefore, a reliable methodology capable to predict in advance the beginning of seizures would have a tremendous impact on the quality of life of these patients and might prevent further complications with the management of this condition. In this end, EEG provides a reliable method to detect the seizures with very good temporal resolution. Besides that, advances in wearable technologies had lead to the creation of the first prototypes of portable EEG sensors coupled to smart – phones and the further connection to servers. Thus, the signal processing in situ is becoming a must. This project is aimed at studying the feasibility to use the software R for statistical analysis of EEG signals in order to perform statistical forecast of epileptic seizures by constructing functional networks based on the cross-correlation of time series from different electrodes. Such functional associations are a result of an emergent neuronal activity of a large amount of neurons, thus, they will be guidance to physicians. A further understanding of the causes will require a combination of biomedical modeling and sensing with fMRI and EEG combined.

Keywords: epilepsy, time series analysis, correlation analysis, functional networks, EEG, risk forecasting