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...

S EIZURE forecasting systems hold promise for improving the quality of life for patients with epilepsy. One proposed forecasting method relies on continuous intracranial electroencephalography (iEEG) recording to look for telltale feature sets in EEG data that suggest imminent seizure threats. In order for EEG –based seizure forecasting systems to work effectively, computational algorithms must...

Beginning in the 1970s engineers designed systems to predict epileptic seizures based upon quantitative changes in the electroencephalogram, which they hypothesized began well in advance of clinical seizure onset. These efforts flourished in the 1990s, as independent laboratories demonstrated evidence of a 'preseizure period' up to 20 min prior to clinical symptoms in patients implanted with in...

In this project, we sought to develop a learning algorithm that identifies EEG time series as pre-ictal (the time just before a seizure occurs) or inter-ictal (the time between seizures) using undersampled data to reduce the energy consumption and bandwidth usage. By training the model with segmented 1 second intervals and predicting seizures with a decision window, we achieved high seizure pre...

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...

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 ...

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...

Although epilepsy surgery is an effective treatment for patients with drug-resistant epilepsy, surgical outcomes vary across patient groups and studies. Identification of reliable prognostic factors for surgical outcome is important for outcome research. In this study, recent systematic reviews and meta-analyses on prediction of seizure outcome have been analyzed, and common predictors of seizu...

Objective To identify the prehospital factors predicting the performance of tracheal intubation (TI) at the emergency department (ED) in patients with convulsive seizure or epilepsy. Methods We performed a retrospective analysis of seizure patients who underwent TI at the ED soon after arrival. The clinical variables obtained in the prehospital setting were reviewed. Patients The study populati...