An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development

• Published in: Applied sciences Vol. 13; no. 17; p. 9571
• Main Authors: Lasefr, Zakareya, Elleithy, Khaled, Reddy, Ramasani Rakesh, Abdelfattah, Eman, Faezipour, Miad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Accuracy; Algorithms; Automation; Brain research; Classification; Convulsions & seizures; Data mining; Decomposition; EEG; Electroencephalography; Epilepsy; epileptic seizure; Fourier transforms; Internet of Things; Machine learning; machine learning algorithms; Methods; Mobile applications; Mobile communications networks; Neural networks; Patients; Problem solving; Seizures (Medicine); Signal processing; Wireless telephone software; Germany
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13179571

Epileptic seizure detection classification distinguishes between epileptic and non-epileptic signals and is an important step that can aid doctors in diagnosing and treating epileptic seizures. In this paper, we studied the existing epileptic seizure detection methods in terms of challenges and processes developed based on electroencephalograph (EEG) signals. To identify the research deficiencies and provide a feasible solution, we surveyed the existing techniques at each phase, including signal acquisition, pre-processing, feature extraction, and classification. Most previous and current research efforts have used traditional features and decomposing techniques. Therefore, in this paper, we introduced an enhanced and efficient epileptic seizure technique using EEG signals, for which we also developed a mobile application for monitoring the classification of EEG signals. The application triggers notifications to all associated users and sends a visual notification should an EEG signal be classified as epileptic. In this research, we have used publicly available EEG data from the University of Bonn. Our proposed method achieved an average accuracy of 98% by utilizing different machine-learning algorithms for classification, and it has outperformed recently published studies. Though there have been other mobile applications for epileptic seizure detection, they have been based on motion and falling detection, as opposed to ours, which was developed based on EEG classification. Our proposed method will have an impact in the medical field, particularly for epilepsy seizure monitoring as well as in the Human–Computer Interaction fields, majorly in the Brain–Computer Interaction (BCI) applications.