Ahmet AlkanYasar Guneri SahinBekir KarlikAlkan, AhmetKarlik, BekirSahin, Yasar GuneriA SattarBH Kang2025-10-062006978-3-540-49787-597835404978750302-97431611-334910.1007/11941439_992-s2.0-84880922605https://gcris.yasar.edu.tr/handle/123456789/6794https://doi.org/10.1007/11941439_99This paper presents a new design of mobile epilepsy warning system for medical application in telemedical environment. Mobile Epilepsy Warning System (MEWS) consists of a wig with a cap equipped with sensors to get Electroencephalogram (EEG) signals a collector which is used for converting signals to data Global Positioning System (GPS) a Personal Digital Assistant (PDA) which has Global System for Mobile (GSM) module and execute Artificial Neural Network (ANN) software to test current patient EEG data with pre-learned data and a calling center for patient assistance or support. The system works as individual sensors obtain EEG signals from patient who has epilepsy and establishes a communication between the patient and Calling Center (CC) in case of an epileptic attack. MEWS learning process has artificial neural network classifier which consists of Multi Layered Perceptron (MLP) neural networks structure and back-propagation training algorithm.Englishinfo:eu-repo/semantics/closedAccessARTIFICIAL NEURAL-NETWORK, WAVELET TRANSFORM, SEIZURE DETECTION, TELEMEDICINE, PATIENTConference Object