Abstract
Epilepsy is a chronic brain disease that affects around 50 million people worldwide. This disease is characterized by recurrent seizures, which are brief episodes of involuntary movement that may involve a part of the body (partial) or the entire body (generalized) and are sometimes accompanied by loss of consciousness. Seizure episodes are a result of excessive electrical discharges in a group of brain cells. Different parts of the brain can be the site of such discharges. Seizures can vary from the briefest lapses of attention or muscle jerks to severe and prolonged convulsions. Seizures can also vary in frequency, from less than 1 per year to several per day. Seizure prediction systems can be life changing for patients with epileptic seizures. By accurately identifying the periods in which seizure occurrence has a higher chance of happening we can help epileptic patients live a more normal life. In this paper we aimed to find supervised machine learning (ML) algorithms to predict the risk of seizure happening. We trained multiple classifiers including some pre-trained models using both time and frequency domain predictors from the intracranial electroencephalogram (iEEG) signals. The results are compared the performance measures in this study on a case by case basis. Our algorithm can be easily implemented in a wearable seizure warning device in conjunction with an implantable iEEG sensor. A hand-held personal advisory device can alert the patient of a possible epileptic seizure.
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References
Hebert, L.E., Scherr, P.A., Bienias, J.L., Bennett, D.A., Evans, D.A.: Alzheimer disease in the US population: prevalence estimates using the 2000 census. Arch. Neurol. (2003)
Acharya, U.R., Vinitha Sree, S., Swapna, G., Martis, R.J., Suri, J.S.: Automated EEG analysis of epilepsy: a review. Knowl. Based Syst. (2013)
Fisher, R.S., Van Emde Boas, W., Blume, W., Elger, C., Genton, P., Lee, P., Engel, J.: Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) (2005)
Guénot, M.: Surgical treatment of epilepsy: outcome of various surgical procedures in adults and children. Rev. Neurol. (2004)
Wang, Y., Zhou, W., Yuan, Q., Li, X., Meng, Q., Zhao, X., Wang, J.: Comparison of ictal and interictal EEG signals using fractal features. Int. J. Neural Syst. (2013)
Lisboa, P.J., Taktak, A.F.: The use of artificial neural networks in decision support in cancer: a systematic review. Neural Netw. (2006)
Barakat, N., Bradley, A.P., Barakat, M.N.H.: Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans. Inf. Technol. Biomed. (2010)
Patle, A., Chouhan, D.S.: SVM kernel functions for classification. In: 2013 International Conference on Advances in Technology and Engineering, ICATE 2013 (2013)
Shoeb, A., Guttag, J.: Application of machine learning to epileptic seizuredetection. In: Proceedings, 27th International Conference on Machine Learning, ICML 2010 (2010)
Xu, Y., Zhu, Q., Fan, Z., Qiu, M., Chen, Y., Liu, H.: Coarse to fine K nearest neighbor classifier. Pattern Recogn. Lett. (2013)
Fushiki, T.: Estimation of prediction error by using K-fold cross-validation. Stat. Comput. (2011)
Verikas, A., Bacauskiene, M.: Feature selection with neural networks. Pattern Recogn. Lett. (2002)
Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., Zhang, Z.: The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015)
Djebbari, A., Reguig, F.B.: Short-time fourier transform analysis of the phonocardiogram signal. In: Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems (2000)
Zawawi, T.N., Abdullah, A.R., Shair, E.F., Halim, I., Rawaida, O.: Electromyography signal analysis using spectrogram. In: Proceeding - 2013 IEEE Student Conference on Research and Development, SCOReD 2013 (2015)
McNames, J.N., Fraser, A.M.: Obstructive sleep apnea classification based on spectrogram patterns in the electrocardiogram. In: Computers in Cardiology (2000)
Acknowledgments
The data used in this paper is from a Kaggle competition. This Kaggle competition was sponsored by MathWorks, the National Institutes of Health (NINDS), the American Epilepsy Society and the University of Melbourne, and organized in partnership with the Alliance for Epilepsy Research, the University of Pennsylvania and the Mayo Clinic. e3.
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Hadipour, S., Tokhmpash, A., Shafai, B. (2021). A Comparative Study on Epileptic Seizure Detection Methods. In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_1
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DOI: https://doi.org/10.1007/978-3-030-51041-1_1
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