Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG sign...

pg 1 LIST OF ABBREVIATIONS IN ALPHABETIC ORDER pg 2 INTRODUCTION 1. Historical background. pg 3 1.1 Epileptic seizures. pg 4 2. Epilepsy and electroencephalography. pg 7 2.1 Spatial-temporal dynamics in epilepsy. pg 8 • PREDICTION 3. Detection and prediction of seizures on scalp-EEG data. pg 9 3.1 Linear and non linear measures to predicting of seizures. pg 10 • CONTROL 4. Seizures control. pg ...

Most of the current epileptic seizure prediction algorithms require much prior knowledge of a patient’s pre-seizure electroencephalogram (EEG) patterns. They are impractical to be applied to a wide range of patients due to a very high inter-individual variability of EEG patterns. This paper proposes an adaptive prediction framework, which is capable of accumulating knowledge of pre-seizure EEG ...

OBJECTIVE Abnormal synchronization of neurons plays a central role for the generation of epileptic seizures. Therefore, multivariate time series analysis techniques investigating relationships between the dynamics of different neural populations may offer advantages in predicting epileptic seizures. METHODS We applied a phase and a lag synchronization measure to a selected subset of multicont...

Background: Serum prolactin level has been used as an alternative determinant to help differentiate epileptic from non-epileptic seizures. We aimed to analyze the association between serum prolactin concentration and epileptic seizure versus febrile seizure as well as seizure duration, single versus multiple seizure attacks and time lapse between seizure onset and blood sampling. Methods: Seven...

An index observation where a dog was trained to alert to, as well as respond to, human tonic-clonic seizures led to further research and refinement of training techniques. This was followed by anecdotal reports of pet dogs spontaneously anticipating human epileptic seizures. An industry has since developed training Seizure-Alert Dogs (SADs) to give humans warnings of their seizures. In some cas...

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure predict...

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure predict...

Epilepsy is the second most common serious brain disorder after stroke. Worldwide, at least 40 million people or 1% of population currently suffer from epilepsy. Approximately 25-30% of epileptic patients remain unresponsive to antiepileptic drug treatment, which is the standard therapy for epilepsy. There is a growing interest in predicting epileptic seizures using intracranial electroencephal...

Many studies have focused on the nonlinear analysis of electroencephalography (EEG) mainly for the characterization of epileptic brain states. It is assumed that at least two states of the epileptic brain are possible: the interictal state characterized by a normal apparently random, steady-state EEG ongoing activity; and the ictal state that is characterized by paroxysmal occurrence of synchro...