Abstract
Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
McGrogan, N., Neural Network Detection of Epileptic Seizures in the Electroencephalogram, 1999, http://www.new.ox.ac.uk/~nmcgroga/work/transfer/.
Gotman, J., Automatic recognition of epileptic seizures in the EEG. Electroencephalogr. Clin. Neurophysiol. 54:530–540, 1982.
Murro, A. M., King, D. W., Smith, J. R., Gallagher, B. B., Flanigin, H. F., and Meador, K., Computerized seizure detection of complex partial seizures. Electroencephalogr. Clin. Neurophysiol. 79:330–333, 1991.
Qu, H., and Gotman, J., A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: Possible use as a warning device. IEEE trans. Biomed. Eng. 44(2):115–122, 1997.
Weng, W., and Khorasani, K., An adaptive structure neural network with application to EEG automatic seizure detection. Neural Netw. 9(7):1223–1240, 1996.
Gotman, J., and Wang, L., State-dependent spike detection: Concepts and preliminary results. Electroencephalogr. Clin. Neurophysiol. 79:11–19, 1991.
Pradhan, N., Sadasivan, P. K., and Arunodaya, G. R., Detection of seizure activity in EEG by an artificial neural network: A preliminary study. Comput. Biomed. Res. 29:303–313, 1996.
Nigam, V. P., and Graupe, D., A neural-network-based detection of epilepsy. Neurol. Res. 26:55–60, 2004.
Viertio-Oja, H., Maja, V., Sarkela, M., Talja, P., Tenkanen, N., Tolvanen-Laakso, H., Paloheimo, M., Vakkuri, A., Yli-Hankala, A., and Merilainen, P., Description of the Entropy™ algorithm as applied in the Datex-Ohmeda S/5™ Entropy Module. Acta Anaesthesiol. Scand. 48:154–161, 2004.
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and 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. Phys. Rev. E 64:1–8, 2001.
Demuth, H., and Beale, M., Neural Network Toolbox (for use with Matlab), Mathworks, Natick, Massachusetts, 2000.
Srinivasan, V., Eswaran, C., and Sriraam, N., Epileptic detection using artificial neural networks. In Proceedings of the 7th Biennial International IEEE Conference in Signal Processing and Communications, 2004.
Kiymik, M. K., Subasi, A., and Ozcalik, H. R., Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizure. J. Med. Syst. 28(6):511–522, 2004.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Srinivasan, V., Eswaran, C. & Sriraam, a.N. Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features. J Med Syst 29, 647–660 (2005). https://doi.org/10.1007/s10916-005-6133-1
Issue Date:
DOI: https://doi.org/10.1007/s10916-005-6133-1