A novel mobile epilepsy warning system

Authors:
Ahmet Alkan;Yasar Guneri Sahin;Bekir Karlik
Affiliations:
Department of Computer Engineering, Yasar University, Izmir, Turkey;Department of Computer Engineering, Yasar University, Izmir, Turkey;Department of Computer Engineering, Buyukcekmece, Fatih University, Istanbul, Turkey
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 3
Cited 0

Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing

Neural Networks
Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network

Expert Systems with Applications: An International Journal
Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

This 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.