Joining the benefits: Combining epileptic seizure prediction methods

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Joining the benefits: combining epileptic seizure prediction methods.

PURPOSE In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable predictions of epileptic seizures. Because individual methods for seizure prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms cap...

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Epilepsy is a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. The sudden and seemingly unpredictable nature of seizures is one of the most compromising aspects of the disease epilepsy. Most epilepsy patients only spend a marginal part of their time actually having ...

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Epileptic Seizure Prediction Using Hybrid Feature Selection

A comprehensive research of Electroencephalography (EEG) is carried out on Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) domains. In this scenario, the hybrid feature extraction is performed by utilizing entropy features like Shannon entropy, log-energy entropy and Renyi entropy. Generally, the entropy measures are effective in evaluation of non-linear interrelation an...

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Special issue on epileptic seizure prediction

E PILEPSY is the most common neurological disorder after stroke, and affects almost 60 million people worldwide. Medications control seizures in only 2/3 of those affected, and another 7%–8% are potentially curable by surgery. This leaves fully 25%, or 15 million people whose seizures cannot be controlled by any available therapy. Over the past ten years engineers and quantitative scientists ha...

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ژورنال

عنوان ژورنال: Epilepsia

سال: 2010

ISSN: 0013-9580

DOI: 10.1111/j.1528-1167.2009.02497.x