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A Review on Epileptic Seizure Detection and Prediction Using Soft Computing Techniques

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Smart Techniques for a Smarter Planet

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 374))

Abstract

Epilepsy is a disorder of the central nervous system in which a considerably large number of neurons at a certain instance of time show abnormal electrical activity. Worldwide according to estimation by WHO, roughly 50 million people are affected by epilepsy that includes patients from infants and adolescent to adults. The most common tool that is used for the determining epileptic seizure after its manifestation is the electroencephalogram (EEG). A certain number of changes occur in behavior as well as perception during epilepsy attack that can be noted chronologically. Generally in human being the manifestation of seizure is illustrated by ictal patterns. The onset of seizures are marked by the change in the ictal phase and this change helps in understanding the underlying mechanism of brain during an epileptic attack so that diagnosis and treatment can be bestowed upon the patient. Over the years, research is going in this domain to develop algorithms that can differentiate between seizure and non-seizure phases and develop mechanism that can detect and predict seizure before its onset. In this paper, we have extensively studied different soft computing techniques that have been developed over the years and have addressed the major singular problem of detection and prediction of an epilepsy seizure before its manifestation so that the after effects of the seizure can be minimized. The range of techniques that have been used for this purpose ranges from artificial neural network, support vector machines, adaptive neuro-fuzzy inference system, genetic algorithm and so on. Comparative study of these different soft computing techniques has been studied to obtain an idea about the performance and accuracy of the various methods. The paper also brings forth the practicality of the techniques in real life scenario and identifies the shortcomings as well as determines the area in this domain that holds prospective for future scope of work. Epilepsy research is a fascinating area that comes with numerous potentials for developing automated systems that would open new avenues for treating the patient. Therefore, in this paper a review is done on different soft computing techniques to understand where our research scenario stands and what improvisations can be made that would not only provide better solution and enhance the quality of living of the epilepsy patients but will also find effective answers to the unanswerable questions.

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References

  1. Aarabi, A., FazelRezai, R., Aghakhani, Y.: A fuzzy rule-based system for epileptic seizure detection in intracranial EEG. Clin. Neurophysiol. 120(9), 1648–1657 (2009)

    Article  Google Scholar 

  2. Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54(2), 205–211 (2007)

    Article  Google Scholar 

  3. Adeli, H., Ghosh-Dastidar, S.: Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology. CRC press, Baco Raton (2010)

    Google Scholar 

  4. Alotaiby, T.N., Alshebeili, S.A., Alshawi, T., et al.: EEG seizure detection and pre-diction algorithms: a survey. EURASIP J. Adv. Signal Process. 2014(1), 183 (2014)

    Article  Google Scholar 

  5. Barua, S., Begum, S.: A review on machine learning algorithms in handling EEG artifacts. In: The Swedish AI Society (SAIS) Workshop SAIS, vol. 14. Stockholm, Sweden, May (2014)

    Google Scholar 

  6. Bizopoulos, P.A., Tsalikakis, D.G., Tzallas, A.T.: EEG epileptic seizure detection using k-means clustering and marginal spectrum based on ensemble empirical mode decomposition. In: IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). Chania, Greece, November (2013)

    Google Scholar 

  7. Conradsen, I.: Detection of epileptic seizures with multi-modal signal processing. Dissertation, DTU Elektro (2013)

    Google Scholar 

  8. Correa, A.G., Laciar, E., Patino, H.D., et al.: Artifact removal from EEG signals using adaptive filters in cascade. J. Phys. Conf. Ser. 90(1), 012081 (2007). IOP Publishing

    Article  Google Scholar 

  9. Fathima, T., Khan, Y.U., Bedeeuzzaman, M., et al.: Discriminant analysis for epileptic seizure detection. In: 2011 International Conference on Devices and Communications (ICDeCom) (2011)

    Google Scholar 

  10. Fergus, P., Hussain, A., Hignett, D., et al.: A machine learning system for automat-ed whole-brain seizure detection. Appl. Comput. Inform. 12(1), 70–89 (2016)

    Article  Google Scholar 

  11. Gajic, D., Djurovic, Z., DiGennaro, S., et al.: Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed. Eng. Appl. Basis Commun. 26(02), 1450021 (2014)

    Article  Google Scholar 

  12. Giannakakis, G., Sakkalis, V., Pediaditis, M., et al.: Methods for seizure detection and prediction: an overview. Modern Electroencephalographic Assessment Techniques, pp. 131–157. Humana Press, New York (2014)

    Google Scholar 

  13. He, P., Kahle, M., Wilson, G., Russell, C.: Removal of ocular artifacts from EEG: a comparison of adaptive filtering method and regression method using simulated data. In: Engineering in Medicine and Biology Society, IEEE-EMBS 2005, January (2006)

    Google Scholar 

  14. Hegde, N.N., Nagananda, M.S., Harsha, M.: EEG signal classification using K-means and fuzzy C means clustering methods. IJSTE 2(01) (2015)

    Google Scholar 

  15. Iqbal, S., Khan, Y.U., Farooq, O.: Nonlinear analysis of EEG for seizure prediction. In: India Conference (INDICON). IEEE, December (2015)

    Google Scholar 

  16. Kannathal, N., Min, L.C., Acharya, U.R., et al.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80, 187–194 (2006)

    Article  Google Scholar 

  17. Kumar, L., Srivastava, G.: Epilepsy detection using clustering techniques (2014)

    Google Scholar 

  18. Li, Y., Wen, P.: Analysis and classification of EEG signals using a hybrid clustering technique. In: IEEE/ICME International Conference on Complex Medical Engineering (CME), July (2010)

    Google Scholar 

  19. Minasyan, G.R., Chatten, J.B., Chatten, M.J., et al.: Patient-specific early seizure detection from scalp EEG. Journal of clinical neurophysiology: official publication of the. Am. Electroencephalogr. Soc. 27(3), 163 (2010)

    Google Scholar 

  20. Nanthini, B.S., Santhi, B.: Seizure detection using SVM classier on EEG signal. J. Appl. Sci. 14(14), 1658–1661 (2014)

    Article  Google Scholar 

  21. Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert. Syst. Appl. 36(2), 2027–2036 (2009)

    Article  Google Scholar 

  22. Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert. Syst. Appl. 38(10), 13475–13481 (2011)

    Article  Google Scholar 

  23. Orosco, L., Correa, A.G., Laciar, E.: A survey of performance and techniques for automatic epilepsy detection. J. Med. Biol. Eng. 33(6), 526–537 (2013)

    Article  Google Scholar 

  24. Park, Y.S., Hochberg, L.R., Eskandar, E.N., et al.: Adaptive parametric spectral estimation with Kalman smoothing for online early seizure detection. In: Neural Engineering (NER), pp. 1410–1413 (2013)

    Google Scholar 

  25. Parvez, M.Z., Paul, M.: Epileptic seizure detection by analyzing EEG signals using different transformation techniques. Neurocomputing 145, 190–200 (2014)

    Article  Google Scholar 

  26. Physionet CHB-MIT Scalp EEG Database. https://physio-net.org/pn6/chbmit/

  27. Ramgopal, S., ThomeSouza, S., Jackson, M., et al.: Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 37, 291–307 (2014)

    Article  Google Scholar 

  28. Saab, M.E., Gotman, J.: A system to detect the onset of epileptic seizures in scalp EEG. Clin. Neurophysiol. 116(2), 427–442 (2005)

    Article  Google Scholar 

  29. Satapathy, S.K., Jagadev, A.K., Dehuri, S.: an empirical analysis of different machine learning techniques for classification of EEG signal to detect epileptic seizure. Informatica, 41(1) (2017)

    Google Scholar 

  30. Sharmila, A., Geethanjali, P.: DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers. IEEE Access 4, 7716–7727 (2016)

    Article  Google Scholar 

  31. Shoeb, A., Edwards, H., Connolly, J., et al.: Patient-specific seizure onset detection. Epilepsy Behav. 5(4), 483–498 (2004)

    Article  Google Scholar 

  32. Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. Dissertation, Massachusetts Institute of Technology (2009)

    Google Scholar 

  33. Thodoroff, P., Pineau, J., Lim, A.: Learning robust features using deep learning for automatic seizure detection. In: Machine Learning for Healthcare Conference, pp. 178–190, December (2016)

    Google Scholar 

  34. Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using timefrequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)

    Article  Google Scholar 

  35. Xie, S., Lawniczak, A.T., Song, Y., et al.: Feature extraction via dynamic PCA for epilepsy diagnosis and epileptic seizure detection. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP), August (2010)

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, Odisha, India

    Satarupa Chakrabarti, Aleena Swetapadma & Prasant Kumar Pattnaik

Authors
  1. Satarupa Chakrabarti
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  2. Aleena Swetapadma
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  3. Prasant Kumar Pattnaik
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Correspondence to Aleena Swetapadma .

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Editors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Manoj Kumar Mishra

  2. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Bhabani Shankar Prasad Mishra

  3. Department of Computer Science and Engineering, IIT Patna, Patna, Bihar, India

    Yashwant Singh Patel

  4. Department of Computer Science and Engineering, IIT Patna, Patna, Bihar, India

    Rajiv Misra

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Chakrabarti, S., Swetapadma, A., Pattnaik, P.K. (2019). A Review on Epileptic Seizure Detection and Prediction Using Soft Computing Techniques. In: Mishra, M., Mishra, B., Patel, Y., Misra, R. (eds) Smart Techniques for a Smarter Planet. Studies in Fuzziness and Soft Computing, vol 374. Springer, Cham. https://doi.org/10.1007/978-3-030-03131-2_3

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