Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required t...

A robust seizure prediction methodology would enable a "closed-loop" system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998; Ben-Menachem, 2001), while preserving overall battery life of the sys...

The extraction of epileptic seizure waveform from the electroencephalogram (EEG) using Independent Component Analysis (ICA) was demonstrated by James and Lowe. A recent variation, Constrained ICA, allows a supplied reference signal to select a single component to be extracted. We show how this algorithm can be applied to the problem of seizure waveform extraction from EEG signals prior to the v...

Closed-loop neurostimulation devices that stimulate the brain to treat epileptic seizures have shown great promise in treating more than a third of the 2 million people with epilepsy in the United States alone whose seizures are currently nonresponsive to pharmaceutical treatment. Seizure detection algorithms facilitate responsive therapeutic intervention that is believed to increase the effica...

We compare the performance of three support vector machine (SVM) types: weighted SVM, one-class SVM and support vector data description (SVDD) for the application of seizure detection in an animal model of chronic epilepsy. Large EEG datasets (273 h and 91 h respectively, with a sampling rate of 1 kHz) from two groups of rats with chronic epilepsy were used in this study. For each of these EEG ...

In this paper, we present a novel epileptic seizure detection algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onsets that are not associated with rhythmic EEG activity. In this algorithm, spectral and spatial features are extracted from seizure and non-seizure EEG signals by Gabor functions and combined with four extracted features...

Automatic seizure detection is becoming popular in modern epilepsy monitoring units since it assists diagnostic monitoring and reduces manual review of large volumes of EEG recordings. In this paper, we describe the application of machine learning algorithms for building patient-specific seizure detectors on multiple frequency bands of intra-cranial electroencephalogram (iEEG) recorded by a den...

There are a number of approaches for analysing EEG signals in the time, frequency, and timefrequency domains. However, due to the nonstationarity of the EEG signals, the time-frequency methodsproved to be superior. This paper presents a new method for detection of newborn EEG seizure activity in the time-fequency domain. The proposed approach utilises 30-second epochs of EEG signal and analyses...