Enhanced Epilepsy Seizure Detection and Smart Phone APP for Monitoring Seizures Based on EEG Classification
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Authors
Lasefr, Zakareya
Elleithy, Khaled M.
Issue Date
2018-03-23
Type
Other
Language
en_US
Keywords
Electromyography (EEG) , Epilepsy , Mobile application , Monitoring system , Seizure
Alternative Title
Abstract
Automated epilepsy seizure detection is the solution to the limitation and time consuming of manual epilepsy monitoring and detection using EEG signals. We developed a technique for epilepsy seizure detection using EEG signals. The signal will be pre-processed and filtered using multiple filters. Then, the filtered signal will be decomposed into sub-bands. Furthermore, feature extraction is applied; we developed a combined feature consists of combining three features into one. Finally, we used well-known classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nears Neighbor (KNN) to differentiate between epileptic and no epileptic signals, and we achieved an accuracy of 98%. Furthermore, we developed an Android-based smartphone application for monitoring epilepsy detection based on the classification results of the EEG signal. A notification will be sent to the patient, doctors, and family members when an epilepsy seizure occurs. Once the EEG signal is classified as epileptic, the App will display a visual notification indicating that Epileptic Seizure has been detected. Moreover, it will trigger an alarm and send a message notification to all associated phone numbers. Although we are using an EEG signal from a dataset, we have generated both normal and epileptic EEG signals using a waveform generator, and we have displayed those signals on the spectrum analyzer for future real time detection using our Android App.