One of the most challenging tasks with Electroencephalography (EEG) signals is the automated epileptic seizure detection. Traditional automated epileptic seizure detection approaches focus on time or frequency domain to analyse EEG signals. A novel technique for epileptic seizure detection is proposed in this paper, which is based on complex networks synchronous state. The concept of complex ne...

The monitoring of epileptic seizures is mainly done by means of electroencephalogram (EEG) monitoring. Although this method is accurate, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient's movement. This makes long-term home monitoring not feasible. In this paper, the aim is to propose a seizure detection system based on accel...

PURPOSE The study aims to review systematically the quality of evidence supporting seizure detection devices. The unpredictable nature of seizures is distressing and disabling for sufferers and carers. If a seizure can be reliably detected then the patient or carer could be alerted. It could help prevent injury and death. METHODS A literature search was completed. Forty three of 120 studies f...

Epilepsy is a neurological disease that affects the brain and is characterised by repeated seizures. Generalised, focal and unknown are three major types of seizures. Each type has several subgroups. For this reason, seizure detection and classification are expensive and erroneous. Other factors can also affect the detection. For example, patients can have a combination of different seizures or...

OBJECTIVE The study of epileptic electroencephalograph (EEG) dynamics can potentially provide insights into seizure onset, evolution and termination. We propose a new synthetic measure based on time-frequency decomposition to provide detailed characterization of these dynamic changes. METHODS The matching pursuit (MP) method allows for continuous time-frequency decomposition. We have develope...

Determination of the epileptogenic zone is typically attempted by ictal video electroencephalographic (EEG) recordings with scalp electrodes, correlated with other noninvasive studies, particularly neuroimaging. However, conventional scalp EEG gives often equivocal information about the location of seizure origin, or it is incongruent with other tests, leading to the requirement for invasive re...

A novel neonatal seizure detection system is proposed including work in the areas of Independent Component Analysis, feature extraction, probability and classification networks. The system comprises of a preprocessing stage to reduce the effect of EEG artifacts and incorporate multichannel analysis and data reduction. A feature extraction stage examines the EEG using techniques from various sig...

This chapter highlights the current approaches in automated neonatal seizure detection and in particular focuses on classifier based methods. Automated detection of neonatal seizures has the potential to greatly improve the outcome of patients in the neonatal intensive care unit. The electroencephalogram (EEG) is the only signal on which 100% of electrographic seizures are visible and thus is c...