PURPOSE There is a need for a seizure-detection system that can be used long-term and in home situations for early intervention and prevention of seizure related side effects including SUDEP (sudden unexpected death in epileptic patients). The gold standard for monitoring epileptic seizures involves video/EEG (electro-encephalography), which is uncomfortable for the patient, as EEG electrodes a...

Differential operators can detect significant changes in signals. This has been utilized to enhance the contrast of the seizure signatures in depth EEG or ECoG. We have actually taken normalized exponential of absolute value of single or double derivative of epileptic ECoG. This in short we call differential filtering. Windowed variance operation has been performed to automatically detect seizu...

Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of...

Objectives: Electroencephalogram (EEG) signal gives a viable perception about the neurological action of human brain that aids detection epilepsy. The objective this study is to build an accurate automated hybrid model for epileptic seizure detection. Methods: This work develops computer-aided diagnosis (CAD) machine learning which can spontaneously classify pre-ictal and ictal EEG signals. In ...

how to cite this article: taghdiri mm, bakhshandeh bali mk, karimzadeh p, ashrafi mr, tonekaboni sh, ghofrani m. comparative efficacy of zonisamide and pregabalin as an adjunctive therapy in children with refractory epilepsy. iran j child neurol. 2015 winter;9(1):49-55. abstract objective approximately one third of epileptic children are resistant to anticonvulsant drugs. this study evaluates t...

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...

Detection of epileptic seizures on the basis Electroencephalogram (EEG) recordings is a challenging task due to complex, non-stationary and non-linear nature these biomedical signals. In existing literature, number automatic seizure detection methods have been proposed that extract useful features from EEG segments classify them using machine learning algorithms. Some characterizing non-epilept...