Smart phone application development for monitoring epilepsy seizure detection based on EEG signal classification

Abstract

Automated epilepsy seizure detection is the solution to the limitation and time consuming of manual epilepsy monitoring and detection using EEG signals. In this paper 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 non-epileptic signals, and we achieved an accuracy of 97%. 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, and the EEG signal will display on the App. 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.