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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Langfitt, J., Meador, K.: Want to Improve Epilepsy Care? Ask the Patient. Neurology 62, 6–7 (2004)
Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of Eeg Records In An Epileptic Patient Using Wavelet Transform. J. Neuroscience Math. 123, 69–87 (2003)
Alkan, A., Koklukaya, E., Subasi, A.: Automatic Seizure Detection In EEG Using Logistic Regression and Artificial Neural Network. Journal of Neuroscience Methods 148(2), 167–176 (2005)
Misra, U.K., Kalita, J., Mishra, S.K., Yadav, R.K.: Telemedicine in neurology: Underutilized potential. Neurology India 53(1), 27–31 (2005)
Bingham, E., Patterson, V.: Nurse led epilepsy clinics: A telemedicine approach. Journal of Neurology Neurosurgery and Psychiatry 72(2), 216–216 (2002)
Patterson, V., Bingham, E.: Telemedicine for epilepsy: A useful contribution. Epilepsia 46(5), 614–615 (2005)
Subasi, A.: Automatic detection of epileptic seizure using dynamic fuzzy neural networks. Expert Systems with Applications 31(2), 320–328 (2006)
Subasi, A., Alkan, A., Koklukaya, E., Kiymik, M.K.: Wavelet Neural Network Classification Of EEG Signals By Using AR Model With MLE. Preprocessing Neural Networks 18(7), 985–997 (2005)
Subasi, A., Erçelebi, E., Alkan, A., Koklukaya, E.: Comparison of Subspace-Based Methods With AR Parametric Methods In Epileptic Seizure Detection. Computers in Biology and Medicine 36(2), 195–208 (2006)
Ceylan, R., Ozbay, Y.: Comparison of FCM, PCA and WT Techniques for Classification ECG Arrhythmias Using Artificial Neural Network. Expert Systems with Applications, Corrected Proof, available online (in Press, 2006)
Kiymik, M.K., Akin, M., Subasi, A.: Automatic Recognition Of Alertness Level By Using Wavelet Transform And Artificial Neural Network. Journal of Neuroscience Methods 139(2), 231–240 (2004)
Andrzejak, R.G., Lehnertz, K., Rieke, C., Mormann, F., David, P., Elger, C.E.: Indications of Nonlinear Deterministic And Finite Dimensional Structures In Time Series Of Brain Electrical Activity: Dependence On Recording Region And Brain State. Physics Rev. E. 64, 061907 (2001)
Karlik, B., Tokhi, O., Alci, M.: A Novel Technique for Classification of Myoelectric Signals for Prosthesis. In: CD-ROM Proceeding of IFAC 2002, Barcelona (July 2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alkan, A., Sahin, Y.G., Karlik, B. (2006). A Novel Mobile Epilepsy Warning System. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_99
Download citation
DOI: https://doi.org/10.1007/11941439_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
eBook Packages: Computer ScienceComputer Science (R0)