Epilepsy is one of the common neurological disorders characterized by a sudden and recurrent malfunction of the brain that is termed “seizure”, affecting around 65 million individuals worldwide. Epileptic seizure may lead to many injuries such as fractures, submersion, burns, motor vehicle accidents and even death. It is highly possible to prevent these unwanted situations if we can predict/det...

OBJECTIVE Phenobarbital increases electroclinical uncoupling and our preliminary observations suggest it may also affect electrographic seizure morphology. This may alter the performance of a novel seizure detection algorithm (SDA) developed by our group. The objectives of this study were to compare the morphology of seizures before and after phenobarbital administration in neonates and to dete...

Epilepsy is one of the most common neurological disorders, by which around 1% of the people in the world are affected. Unfortunately, 25% of the epilepsy patients cannot be treated sufficiently by antiepileptic drugs and epilepsy surgery. If seizures cannot be well controlled, the patients experience major limitations in their lives. In recent years, open-loop seizure controllers, such as vagus...

OBJECTIVE The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. METHODS EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity s...

Epilepsy is one of the common neurological disorders characterized by a sudden and recurrent malfunction of the brain that is termed “seizure”, affecting over 50 million individuals worldwide. The Electroencephalogram (EEG) is the most influential technique in detection of epileptic seizures. In recent years, many research works have been devoted to the detection of epileptic seizures based on ...

This article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as thes...

The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to nor...

The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component anal...