Research in automatic analysis of EEG for supporting the diagnosis of epileptic seizures took pace in the 1970s. Prior et al [45] suggested the use of a device called cerebral function monitor to demarcate generalized tonic-clonic seizures. These could be identified as a large increase in EEG amplitude followed by an observable decrease and by large EMG activity. Method described by Ives et al ...

s are listed alphabetically by title within topics. Poster Topic Page Number Numbers 1-16 Seizure Prediction and Detection ............................................. Page 30 17-26 Neural Models and Mechanisms .............................................. Page 42 27-30 Epilepsy Connectome .............................................................. Page 50 31-35 Seizure Intervention .........

Epileptic seizures can cause a variety of temporary changes in perception and behavior. In the human EEG they are reflected by multiple ictal patterns, where epileptic seizures typically become apparent as characteristic, usually rhythmic signals, often coinciding with or even preceding the earliest observable changes in behavior. Their detection at the earliest observable onset of ictal patter...

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most co...

OBJECTIVE Retrospective evaluation and comparison of performances of a multivariate method for seizure detection and prediction on simultaneous long-term EEG recordings from scalp and intracranial electrodes. METHODS Two multivariate techniques based on simulated leaky integrate-and-fire neurons were investigated in order to detect and predict seizures. Both methods were applied and assessed ...

This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunte...

A method for the automatic detection of seizure in newborns is presented. The proposed method is derived from the ability to detect changes in signal structure as the newborn EEG changes from the background state to the seizure state. Matching Pursuit decomposition technique, with an overcomplete time−frequency dictionary, is shown to be an adequate technique for detecting changes in signal str...

Differential operators can detect significant changes in signals. This has been utilized to enhance the contrast of the seizure signatures in depth EEG or ECoG. We have actually taken normalized exponential of absolute value of single or double derivative of epileptic ECoG. This in short we call differential filtering. Windowed variance operation has been performed to automatically detect seizu...

The special requirements for a seizure detector suitable for everyday use in terms of cost, comfort, and social acceptance call for alternatives to electroencephalography (EEG)-based methods. Therefore, we developed an algorithm for automatic detection of generalized tonic-clonic (GTC) seizures based on sympathetically mediated electrodermal activity (EDA) and accelerometry measured using a nov...

Previous techniques for seizure detection in newborn are inefficient. The main reason for their relative poor performance resides in their assumption of stationarity of the EEG. To remedy this problem, we use time-frequency distributions (TFD) to analyse and characterise the newborn EEG seizure patterns as a first step toward a time-frequency (TF) based seizure detection and classification sche...