Abstract
Epileptic seizure detection is a challenging research topic. The objective of this research is to analyze the performance of various classification techniques while detecting the epileptic seizure in a shorter time. In this paper, we apply four different types of classifiers-two are black-box (SVM & KNN) and other two are non-black-box (Decision tree & Ensemble) on two epileptic patient seizure data sets. Our finding shows that non-black box classifiers, specifically ensemble classifiers, do better than other classifiers. The experimental results indicate that the ensemble classifier can assist for seizure detection in a shorter epoch length of time (i.e., 0.5 s) with high accuracy rate. Significantly in comparison to other classifiers the ensemble classifier provides high accuracy and less chance of false detection rate.
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References
CHB-MIT scalp EEG database. https://physionet.org/pn6/chbmit/. Accessed 20 June 2015
Acharya, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.H., Suri, J.S.: Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4), 401–408 (2012)
Aditya, S., Tibarewala, D.: Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data. Int. J. Artif. Intell. Soft Comput. 3(2), 143–164 (2012)
Adnan, M.N., Islam, M.Z.: Forest CERN: a new decision forest building technique. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS, vol. 9651, pp. 304–315. Springer, Cham (2016). doi:10.1007/978-3-319-31753-3_25
Birjandtalab, J., Pouyan, M.B., Cogan, D., Nourani, M., Harvey, J.: Automated seizure detection using limited-channel EEG and non-linear dimension reduction. Comput. Biol. Med. 82, 49–58 (2017)
de Boer, H.M., Mula, M., Sander, J.W.: The global burden and stigma of epilepsy. Epilepsy Behav. 12(4), 540–546 (2008)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). http://dx.doi.org/10.1023/A:1010933404324
Chen, C.L., Liu, J.L., Syu, J.Y.: Application of chaos theory and data mining to seizure detection of epilepsy
Donos, C., Dümpelmann, M., Schulze-Bonhage, A.: Early seizure detection algorithm based on intracranial EEG and random forest classification. Int. J. Neural Syst. 25(05), 1550023 (2015)
Dorai, A., Ponnambalam, K.: Automated epileptic seizure onset detection. In: 2010 International Conference on Autonomous and Intelligent Systems (AIS), pp. 1–4. IEEE (2010)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. American Association for Artificial Intelligence, Menlo Park (1996)
Fergus, P., Hussain, A., Hignett, D., Al-Jumeily, D., Abdel-Aziz, K., Hamdan, H.: A machine learning system for automated whole-brain seizure detection. Appl. Comput. Inf. 12(1), 70–89 (2016)
Furness, J.B., Costa, M.: The enteric nervous system. Churchill Livingstone Edinburgh etc. (1987)
Gorunescu, F.: Data Mining: Concepts, Models and Techniques, vol. 12. Springer Science & Business Media, Heidelberg (2011). doi:10.1007/978-3-642-19721-5
Islam, M.Z., D’Alessandro, S., Furner, M., Johnson, L., Gray, D., Carter, L.: Brand switching pattern discovery by data mining techniques for the telecommunication industry in Australia. Australas. J. Inf. Syst. 20 (2016)
Islam, M.Z., Giggins, H.: Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees. In: Proceedings of the Ninth Australasian Data Mining Conference, vol. 121, pp. 195–204. Australian Computer Society, Inc. (2011)
Li, J., Liu, H.: Ensembles of cascading trees. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 585–588. IEEE (2003)
Logesparan, L., Casson, A.J., Rodriguez-Villegas, E.: Optimal features for online seizure detection. Med. Biol. Eng. Comput. 50(7), 659–669 (2012)
Orellana, M.P., Cerqueira, F.: Personalized epilepsy seizure detection using random forest classification over one-dimension transformed EEG data. bioRxiv p. 070300 (2016)
Powers, D.M.: Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation (2011)
Rosenow, F., Klein, K.M., Hamer, H.M.: Non-invasive EEG evaluation in epilepsy diagnosis. Expert Rev. Neurother. 15(4), 425–444 (2015)
Shoeb, A., Edwards, H., Connolly, J., Bourgeois, B., Treves, S.T., Guttag, J.: Patient-specific seizure onset detection. Epilepsy Behav. 5(4), 483–498 (2004)
Siddiqui, M.K., Islam, M.Z.: Data mining approach in seizure detection. In: 2016 IEEE Region 10 Conference (TENCON), Singapore, pp. 3579–3583. Institute of Electrical and Electronics Engineers (IEEE), November 2016
Siddiqui, M.K., Islam, M.Z., Kabir, M.A.: Brain data mining on an ECoG dataset for Quick Seizure detection and Localization, March 2017. Submitted to Neural Computing and Applications
Teplan, M., et al.: Fundamentals of EEG measurement. Meas. Sci. Rev. 2(2), 1–11 (2002)
Zhang, Y., Zhang, Y., Wang, J., Zheng, X.: Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput. Appl. 26(5), 1217–1225 (2014)
Almazyad, A.S., Ahamad, M.G.: Siddiqui. M.K., Almazyad, A.S.: Effective hypertensive treatment using data mining in Saudi Arabia. J. Clin. Monit. Comput. 24(6), 391–401 (2010). Springer
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We acknowledge the contribution of Mr. Sajid Saeed Khan, Faculty member of the English Department of Amiruddaula Islamia College, Lucknow University, Lucknow in carefully proof reading the paper.
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Siddiqui, M.K., Islam, M.Z., Kabir, M.A. (2017). Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_27
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DOI: https://doi.org/10.1007/978-3-319-69179-4_27
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