Efficient EEG analysis for seizure monitoring in epileptic patients

Epilepsy is a crucial neurological disorder in which patients experience epileptic seizure caused by abnormal electrical discharges from the brain. It is highly common in children and adults at the age of 65-70. Around 1 % of the world´s population is affected by this disease. The mechanism of epilepsy is still incomprehensible to researchers; however, 80% of the seizure activity can be treated effectively if proper diagnosis is performed. This disease mostly leads to uncontrollable movements, convulsions and loss of conscious and contends the patient to increased possibility of accidental injury and even death. As a result, monitoring the person with epilepsy from being exposed to the danger is among the basic death to life transformation solutions. In this paper, we propose the most important methodologies that could be implemented in hardware for monitoring an epileptic patient. Many studies show that, Electroencephalogram (EEG) is the most important signal used by physicians in assessing the brain activities and diagnosing different brain disorders. This study is based on different EEG datasets that were obtained and described by researchers for analysis and diagnosis of epilepsy. Butterworth bandpass filters are implemented and used to preprocess and decompose the EEG signal into five different EEG frequency bands (delta, theta, alpha, beta, and gamma). In addition, different features such as energy, standard deviation and entropy are then computed and extracted from each Δ, Θ, α, β and γ sub-band. Furthermore, the extracted features are then fed to a supervised learning classifier; support vector machine (SVM); in order to detect the epileptic events and identify if the acquired signal is corresponding to seizure or not according to the objective of this research. If seizure is experienced, appropriate monitoring should be taken in action. Experimental results on a number of subjects confirm 95% classification accuracy- of the proposed work.