A Method for Detecting Epileptic Seizure in Pediatrics Patients Based on EEG Signals

Epilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by WHO. The most common non-invasive tool for studying the brain activity of epileptic patient is the electroencephalogram (EEG). Determining the onset of seizures accurately is still elusive, and over the years, developing effective techniques to monitor epilepsy is progressive. In this work, pediatric patients with history of intractable epilepsy have been studied. The EEG signals used here belong to the scalp EEG database of Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT). For determining between seizure and non-seizure signals, discrete Fourier transform (DFT) has been used as the feature extraction techniques. The features extracted are then given to the artificial neural network (ANN) to identify the presence of epileptic behavior in the signals. After designing the epileptic seizure detector, a setup has been developed using MATALB/Simulink for real-time applications that recorded an accuracy of 98.6% with specificity and sensitivity of 98.1% and 99.2%, respectively. In this work, an improved method is proposed to provide better solution and enhance the quality of living of the pediatric epilepsy patients.