Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algori...

Prediction of post-surgical seizure relief and potential cognitive deficits secondary to anterior temporal lobectomy (ATL) are important to pre-surgical planning. Although the intracarotid amobarbital test (IAT) is predictive of post-ATL seizure outcome, development of non-invasive and more precise means for determining post-ATL seizure relief are needed. We previously reported on a technique u...

A rapidly growing number of studies deals with the prediction of epileptic seizures. For this purpose, various techniques derived from linear and nonlinear time series analysis have been applied to the electroencephalogram of epilepsy patients. In none of these works, however, the performance of the seizure prediction statistics is tested against a null hypothesis, an otherwise ubiquitous conce...

OBJECTIVE To develop a prospective method for optimizing seizure prediction, given an array of implanted electrodes and a set of candidate quantitative features computed at each contact location. METHODS The method employs a genetic-based selection process, and then tunes a probabilistic neural network classifier to predict seizures within a 10 min prediction horizon. Initial seizure and inte...

This paper deals with the design of low power low noise neural signal amplifier for Epileptic Seizure Prediction. The advent of Micro-electro Arrays has driven the need for implantable electronic circuitry to detect those Extracellular neural signals (ENG). We proposed a preamplifier of fully differential Low Noise Amplifier (LNA) with gm boosting in order to enhance the gain as well as reduce ...

This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysi...

Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of...

Several procedures have been proposed to be capable of predicting the occurrence of epileptic seizures. Up to now, all proposed algorithms are far from being sufficient for a clinical application. This is, however, often not obvious when results of seizure prediction performance are reported. Here, we discuss impacts of long prediction horizons with respect to clinical needs and the strain on p...