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 ...

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...

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...

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...

PURPOSE Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Mor...

The most common technology for seizure detection is with electroencephalography (EEG), which has low spatial resolution and minimal depth discrimination. Optical techniques using near-infrared (NIR) light have been used to improve upon EEG technology and previous research has suggested that optical changes, specifically changes in near-infrared optical scattering, may precede EEG seizure onset ...

Long term video electroencephalography (EEG) in epilepsy monitoring units (EMU) is used to diagnose and treat patients with epilepsy. Injury occurs in the EMU, including reports of death. No standardized patient safety protocols exist. Our objective is to determine the frequency and contributing factors to injury in the EMU. We reviewed medical records and video EEG of patients with epilepsy ad...