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Grasshopper optimization algorithm–based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals

  • Gurwinder Singh
  • Birmohan Singh
  • Manpreet KaurEmail author
Original Article

Abstract

Epilepsy is one of the most common neurological disease worldwide. It is diagnosed by analyzing a long electroencephalogram (EEG) recording in a clinical environment, which may be much prone to errors and a time-consuming task. In this paper, a methodology for the classification of an epileptic seizure is proposed for analyzing EEG signals. EEG signal is decomposed into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). A fusion, of the extracted non-linear and spike-based features from each of the IMF signals, is made. The parameters of five machine learning algorithms; k-nearest neighbor (k-NN), extreme learning machine (ELM), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) are optimized, as well as a set of the significant features is chosen using grasshopper optimization algorithm (GOA). These classifiers with their optimized parameters are ensembled together for the classification of epileptic seizures. The results show that ensemble classifier performs better than individual classifier. A comparison of the proposed methodology with state of the art epileptic seizure detection techniques is also made for validation.

Graphical abstract

Keywords

Epilepsy Intrinsic mode functions Empirical mode decomposition k-Nearest neighbor Extreme learning machine Random forest Support vector machine Artificial neural network Grasshopper optimization algorithm 

Notes

References

  1. 1.
    Epilepsy (2017) http://www.who.int/mediacentre/factsheets/fs999/en/. Accessed 25 Oct 2017
  2. 2.
    Indian Epilepsy Centre (2018) http://www.indianepilepsycentre.com/faqs-incidence.html. Accessed 6 Feb 2018
  3. 3.
    Subasi A, Alkan A, Koklukaya E, Kiymik MK (2005) Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Netw 18:985–997.  https://doi.org/10.1016/j.neunet.2005.01.006 CrossRefPubMedGoogle Scholar
  4. 4.
    Sharma R, Pachori RB (2015) Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42:1106–1117.  https://doi.org/10.1016/j.eswa.2014.08.030 CrossRefGoogle Scholar
  5. 5.
    Stacey WC, Litt B (2008) Technology insight: neuroengineering and epilepsy—designing devices for seizure control. Nat Clin Pract Neurol 4:190–201.  https://doi.org/10.1038/ncpneuro0750 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Tzallas AT, Tsipouras MG, Tsalikakis DG et al (2012) Automated epileptic seizure detection methods: a review study. In: Stevanovic D (ed) Epilepsy-histological, electroencephalographic and psychological aspects. INTECH Open Access Publisher, CroatiaGoogle Scholar
  7. 7.
    Das AB, Bhuiyan MIH (2016) Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed Signal Process Control 29:11–21.  https://doi.org/10.1016/j.bspc.2016.05.004 CrossRefGoogle Scholar
  8. 8.
    Sharma R, Pachori RB, Rajendra Acharya U (2015) An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17:5218–5240.  https://doi.org/10.3390/e17085218 CrossRefGoogle Scholar
  9. 9.
    Sharma R, Pachori RB, Acharya UR (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17:669–691.  https://doi.org/10.3390/e17020669 CrossRefGoogle Scholar
  10. 10.
    Sharma M, Dhere A, Pachori RB, Acharya UR (2017) An automatic detection of focal EEG signals using new class of time–frequency localized orthogonal wavelet filter banks. Knowledge-Based Syst 118:217–227.  https://doi.org/10.1016/j.knosys.2016.11.024 CrossRefGoogle Scholar
  11. 11.
    Sharma R, Kumar M, Pachori RB, Acharya UR (2017) Decision support system for focal EEG signals using tunable-Q wavelet transform. J Comput Sci 20:52–60.  https://doi.org/10.1016/j.jocs.2017.03.022 CrossRefGoogle Scholar
  12. 12.
    Bhattacharyya A, Pachori RB, Acharya UR (2017) Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy 19.  https://doi.org/10.3390/e19030099
  13. 13.
    Gupta V, Priya T, Yadav AK, Pachori RB, Rajendra Acharya U (2017) Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recogn Lett 94:180–188.  https://doi.org/10.1016/j.patrec.2017.03.017 CrossRefGoogle Scholar
  14. 14.
    Arunkumar AN, Ramkumar RK, Venkatraman VV et al (2017) Classification of focal and non focal EEG using entropies. Pattern Recogn Lett 94:112–117.  https://doi.org/10.1016/j.patrec.2017.05.007 CrossRefGoogle Scholar
  15. 15.
    Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal, Image Video Process 8:1323–1334.  https://doi.org/10.1007/s11760-012-0362-9 CrossRefGoogle Scholar
  16. 16.
    Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133:271–279.  https://doi.org/10.1016/j.neucom.2013.11.009 CrossRefGoogle Scholar
  17. 17.
    Acharya UR, Yanti R, Zheng JW et al (2013) Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst 23:1350009.  https://doi.org/10.1142/S0129065713500093 CrossRefPubMedGoogle Scholar
  18. 18.
    Song Y, Zhang J (2013) Automatic recognition of epileptic EEG patterns via extreme learning machine and multiresolution feature extraction. Expert Syst Appl 40:5477–5489.  https://doi.org/10.1016/j.eswa.2013.04.025 CrossRefGoogle Scholar
  19. 19.
    Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng KH, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7:401–408.  https://doi.org/10.1016/j.bspc.2011.07.007 CrossRefGoogle Scholar
  20. 20.
    Nicolaou N, Georgiou J (2012) Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39:202–209.  https://doi.org/10.1016/j.eswa.2011.07.008 CrossRefGoogle Scholar
  21. 21.
    Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193:156–163.  https://doi.org/10.1016/j.jneumeth.2010.08.030 CrossRefPubMedGoogle Scholar
  22. 22.
    Kumar SP, Sriraam N, Benakop PG, Jinaga BC (2010) Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl 37:3284–3291.  https://doi.org/10.1016/j.eswa.2009.09.051 CrossRefGoogle Scholar
  23. 23.
    Gotman J, Wang LY (1991) State-dependent spike detection: concepts and preliminary results. Electroencephalogr Clin Neurophysiol 79:11–19.  https://doi.org/10.1016/0013-4694(91)90151-S CrossRefPubMedGoogle Scholar
  24. 24.
    Kaur M, Singh G (2017) Classification of seizure prone EEG signal using amplitude and frequency based parameters of intrinsic mode functions. J Med Biol Eng 37:540–553CrossRefGoogle Scholar
  25. 25.
    Djemili R, Bourouba H, Amara Korba MC (2016) Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybern Biomed Eng 36:285–291.  https://doi.org/10.1016/j.bbe.2015.10.006 CrossRefGoogle Scholar
  26. 26.
    Singh G, Kaur M, Singh D (2016) Detection of epileptic seizure using wavelet transformation and spike based features. In: 2015 2nd international conference on recent advances in engineering and computational sciences. RAECS 2015, pp 1–4Google Scholar
  27. 27.
    Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43:807–816.  https://doi.org/10.1016/j.compbiomed.2013.04.002 CrossRefPubMedGoogle Scholar
  28. 28.
    Alam SMS, Bhuiyan MIH (2013) Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J Biomed Heal Informatics 17:312–318.  https://doi.org/10.1109/JBHI.2012.2237409 CrossRefGoogle Scholar
  29. 29.
    Pachori RB, Varun B (2011) Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput Methods Prog Biomed 104:373–381CrossRefGoogle Scholar
  30. 30.
    Subasi A, Gursoy MI, Ismail Gursoy M (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37:8659–8666.  https://doi.org/10.1016/j.eswa.2010.06.065 CrossRefGoogle Scholar
  31. 31.
    Übeyli ED (2009) Combined neural network model employing wavelet coefficients for EEG signals classification. Digit Signal Process A Rev J 19:297–308.  https://doi.org/10.1016/j.dsp.2008.07.004 CrossRefGoogle Scholar
  32. 32.
    Chandaka S, Chatterjee A, Munshi S (2009) Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst Appl 36:1329–1336.  https://doi.org/10.1016/j.eswa.2007.11.017 CrossRefGoogle Scholar
  33. 33.
    Übeyli ED (2008) Wavelet/mixture of experts network structure for EEG signals classification. Expert Syst Appl 34:1954–1962.  https://doi.org/10.1016/j.eswa.2007.02.006 CrossRefGoogle Scholar
  34. 34.
    Güler I, Ubeyli ED (2005) Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods 148:113–121.  https://doi.org/10.1016/j.jneumeth.2005.04.013 CrossRefPubMedGoogle Scholar
  35. 35.
    Pachori RB, Patidar S (2014) Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Prog Biomed 113:494–502.  https://doi.org/10.1016/j.cmpb.2013.11.014 CrossRefGoogle Scholar
  36. 36.
    Mainardi LT, Bianchi LM, Cerutti S (2012) Digital biomedical signal acquisition and processing. In: Liang H, Bronzino JD, Peterson DR (eds) Biosignal processing: principles and practices. CRC press, Boca RatonGoogle Scholar
  37. 37.
    Oweis RJ, Abdulhay EW (2011) Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed Eng Online 10:38–52.  https://doi.org/10.1186/1475-925X-10-38 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Koller D, Sahami M (1996) Toward {optimal} {feature} {selection}. In: International Conference on Machine Learning, pp 284–292Google Scholar
  39. 39.
    Tiwari S, Singh B, Kaur M (2017) An approach for feature selection using local searching and global optimization techniques. Neural Comput Appl 28:2915–2930.  https://doi.org/10.1007/s00521-017-2959-y CrossRefGoogle Scholar
  40. 40.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182Google Scholar
  41. 41.
    Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19:153–158CrossRefGoogle Scholar
  42. 42.
    Peng Y, Wu Z, Jiang J (2010) A novel feature selection approach for biomedical data classification. Comput Biomed Res 43:15Google Scholar
  43. 43.
    Guo L, Rivero D, Dorado J, Munteanu CR, Pazos A (2011) Automatic feature extraction using genetic programming: an application to epileptic {EEG} classification. Expert Syst Appl 38:10425–10436.  https://doi.org/10.1016/j.eswa.2011.02.118 CrossRefGoogle Scholar
  44. 44.
    Chaovalitwongse RCWAY-JFS (2007) On the time series K-nearest neighbor classification of abnormal brain activity. Syst Man Cybern Part A IEEE Trans 37:1005–1016CrossRefGoogle Scholar
  45. 45.
    Chen D, Wan S, Bao FS (2017) Epileptic focus localization using discrete wavelet transform based on interictal intracranial EEG. IEEE Trans neural Syst Rehabil Eng 25:413–425CrossRefPubMedGoogle Scholar
  46. 46.
    Song Y, Crowcroft J, Zhang J (2012) Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Methods 210:132–146.  https://doi.org/10.1016/j.jneumeth.2012.07.003 CrossRefPubMedGoogle Scholar
  47. 47.
    Chen LL, Zhang J, Zou JZ, Zhao CJ, Wang GS (2014) A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection. Biomed Signal Process Control 10:1–10.  https://doi.org/10.1016/j.bspc.2013.11.010 CrossRefGoogle Scholar
  48. 48.
    Donos C, Dümpelmann M, Schulze-Bonhage A (2015) Early seizure detection algorithm based on intracranial EEG and random forest classification. Int J Neural Syst 25:1550023.  https://doi.org/10.1142/S0129065715500239 CrossRefPubMedGoogle Scholar
  49. 49.
    Zhang T, Chen W, Li M (2017) AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier. Biomed Signal Process Control 31:550–559.  https://doi.org/10.1016/j.bspc.2016.10.001 CrossRefGoogle Scholar
  50. 50.
    Aljarah I, Al-Zoubi AM, Faris H et al (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn Comput:1–18Google Scholar
  51. 51.
    Hamad A, Houssein EH, Hassanien AE, Fahmy AA (2018) Hybrid grasshopper optimization algorithm and support vector Machines for automatic seizure detection in EEG signals. In: International conference on advanced machine learning technologies and applications. Springer, Cham, pp 82–91Google Scholar
  52. 52.
    Ibrahim HT, Mazher WJ, Ucan ON, Bayat O (2018) A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets. Neural Comput Appl:1–10.  https://doi.org/10.1007/s00521-018-3414-4
  53. 53.
    Koçer S, Canal MR (2011) Classifying epilepsy diseases using artificial neural networks and genetic algorithm. J Med Syst 35:489–498.  https://doi.org/10.1007/s10916-009-9385-3 CrossRefPubMedGoogle Scholar
  54. 54.
    Hassan R, Cohanim B, de Weck O (2004) A copmarison of particle swarm optimization and the genetic algorithm. Am Inst Aeronaut Astronaut:1–13Google Scholar
  55. 55.
    Yalçin N, Tezel G, Karakuzu C (2015) Epilepsy diagnosis using artificial neural network learned by PSO. Turk J Electr Eng Comput Sci 23:421–432CrossRefGoogle Scholar
  56. 56.
    Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) 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 64:061907.  https://doi.org/10.1103/PhysRevE.64.061907 CrossRefGoogle Scholar
  57. 57.
    Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK (2017) Automated diagnosis of epilepsy using key-point based local binary pattern of EEG signals. IEEE J Biomed Heal Informatics 21:888–896.  https://doi.org/10.1109/JBHI.2016.2589971 CrossRefGoogle Scholar
  58. 58.
    Kannathal N, Choo ML, Acharya UR, Sadasivan PK (2005) Entropies for detection of epilepsy in EEG. Comput Methods Prog Biomed 80:187–194.  https://doi.org/10.1016/j.cmpb.2005.06.012 CrossRefGoogle Scholar
  59. 59.
    Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36:2027–2036CrossRefGoogle Scholar
  60. 60.
    Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A:2297–2301Google Scholar
  61. 61.
    Richman J, Moorman J (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Hear Circ 278:H2039–H2049CrossRefGoogle Scholar
  62. 62.
    Weiting Chen W, Zhizhong Wang Z, Hongbo Xie H, Wangxin Yu W (2007) Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans Neural Syst Rehabil Eng 15:266–272.  https://doi.org/10.1109/TNSRE.2007.897025 CrossRefPubMedGoogle Scholar
  63. 63.
    Rényi A (1961) On measures of entropy and information. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, pp 547–561Google Scholar
  64. 64.
    Chen J, Li G (2014) Tsallis wavelet entropy and its application in power signal analysis. Entropy 16:3009–3025.  https://doi.org/10.3390/e16063009 CrossRefGoogle Scholar
  65. 65.
    Hjorth B (1975) An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalogr Clin Neurophysiol 39:526–530CrossRefPubMedGoogle Scholar
  66. 66.
    Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun 5:3–55CrossRefGoogle Scholar
  67. 67.
    Gotman J (1986) Computer analysis of EEG in epilepsy. In: Clinical applications of computer analysis of EEG and other neurophysiological signals. Elsevier, Amsterdam, pp 171–204Google Scholar
  68. 68.
    Gotman J (1984) Automatic recognition of interictal spikes. Electroencephalogr Clin Neurophysiol Suppl 37:93–114Google Scholar
  69. 69.
    Frost JD (1985) Automatic recognition and characterization of epileptiform discharges in the human EEG. J Clin Neurophysiol 2:231–250.  https://doi.org/10.1097/00004691-198507000-00003 CrossRefPubMedGoogle Scholar
  70. 70.
    Chatrian GE, Bergamini L, Dondey M et al (1974) A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalogr Clin Neurophysiol 37:538–548CrossRefGoogle Scholar
  71. 71.
    Van Putten MJAM, Kind T, Visser F, Lagerburg V (2005) Detecting temporal lobe seizures from scalp EEG recordings: a comparison of various features. Clin Neurophysiol 116:2480–2489.  https://doi.org/10.1016/j.clinph.2005.06.017 CrossRefPubMedGoogle Scholar
  72. 72.
    Le Van Quyen M, Martinerie J, Baulac M, Varela F (1999) Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. Neuroreport 10:2149–2155.  https://doi.org/10.1097/00001756-199907130-00028 CrossRefGoogle Scholar
  73. 73.
    Navarro V, Martinerie J, Le Van Quyen M et al (2002) Seizure anticipation in human neocortical partial epilepsy. Brain 125:640–655.  https://doi.org/10.1093/brain/awf048 CrossRefPubMedGoogle Scholar
  74. 74.
    Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47.  https://doi.org/10.1016/j.advengsoft.2017.01.004 CrossRefGoogle Scholar
  75. 75.
    Spall JC (2005) Introduction to stochastic search and optimization: estimation, simulation, and control. John Wiley & SonsGoogle Scholar
  76. 76.
    Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287.  https://doi.org/10.1016/S0045-7825(01)00323-1 CrossRefGoogle Scholar
  77. 77.
    Devijver PA, Kittler J (1982) Pattern recognition. A statistical approach. Prentice hallGoogle Scholar
  78. 78.
    Guang-Bin H, Qin-Yu Z, Chee-Kheong S (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of International Joint Conference on Neural Networks, pp 985–990Google Scholar
  79. 79.
    Huang G-BG-B, Zhu Q-YQ-Y, Siew CC-KC-K et al (2006) Extreme learning machine : theory and applications. Neurocomputing 70:489–501.  https://doi.org/10.1016/j.neucom.2005.12.126 CrossRefGoogle Scholar
  80. 80.
    Bin HG, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892.  https://doi.org/10.1109/TNN.2006.875977 CrossRefGoogle Scholar
  81. 81.
    Bin HG, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062.  https://doi.org/10.1016/j.neucom.2007.02.009 CrossRefGoogle Scholar
  82. 82.
    Huang G Bin, Chen L (2008) Enhanced random search based incremental extreme learning machine. In: Neurocomputing. pp 3460–3468Google Scholar
  83. 83.
    Huang GBG-BB, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468.  https://doi.org/10.1016/j.neucom.2007.10.008 CrossRefGoogle Scholar
  84. 84.
    Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weight is more important than the size of the network. IEEE Trans Inf Geom 44:525–536CrossRefGoogle Scholar
  85. 85.
    Breiman L (2001) Random forests. Mach Learn 45:5–32.  https://doi.org/10.1023/A:1010933404324 CrossRefGoogle Scholar
  86. 86.
    Ho TK (1995) Random decision forest. In: Document analysis and recognition. IEEE, pp 278–282Google Scholar
  87. 87.
    Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844.  https://doi.org/10.1109/34.709601 CrossRefGoogle Scholar
  88. 88.
    Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees. Neural Comput 9:1545–1588.  https://doi.org/10.1162/neco.1997.9.7.1545 CrossRefGoogle Scholar
  89. 89.
    Vapnik V (1995) The nature of statistical learning theory. Springer Science & Business MediaGoogle Scholar
  90. 90.
    Kumar S (2004) Neural networks: a class room approach. Tata McGraw-Hill EducationGoogle Scholar
  91. 91.
    Vapnik V (2013) The nature of statistical learning theory. Springer, New YorkGoogle Scholar
  92. 92.
    Begg RK, Palaniswami M, Owen B (2005) Support vector machines for automated gait classification. IEEE Trans Biomed Eng 52:828–838.  https://doi.org/10.1109/TBME.2005.845241 CrossRefPubMedGoogle Scholar
  93. 93.
    Lahmiri S (2011) A comparative study of back propagation algorithms in financial prediction. Int J Comput Sci Eng Appl 1:15–21Google Scholar
  94. 94.
    Kisi O, Uncuoğlu E (2005) Comparison of three back-propagation training algorithms for two case studies. NDIAN J Eng Mater Sci 12:434–442Google Scholar
  95. 95.
    Riedmiller M, Braun H (1993) A direct adaptive method for faster backropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, pp 586–591CrossRefGoogle Scholar
  96. 96.
    Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning supervised learning. Neural Netw 6:525–533CrossRefGoogle Scholar
  97. 97.
    Hagan MT, Demuth HB, Beale MH, De Jesus O (1996) Neural Network Design. PWS Publishing, BostonGoogle Scholar
  98. 98.
    Battiti R (1992) First- and second order methods for learning: between steepest descent and Newton’s method. Neural Comput 4:141–166CrossRefGoogle Scholar
  99. 99.
    Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33:1–39.  https://doi.org/10.1007/s10462-009-9124-7 CrossRefGoogle Scholar
  100. 100.
    Polikar R (2006) Ensemble based systems in decision making. IEEE Cir Syst Mag 6:21–45CrossRefGoogle Scholar
  101. 101.
    Abualsaud K, Mahmuddin M, Saleh M, Mohamed A (2015) Ensemble classifier for epileptic seizure detection for imperfect EEG data. ScientificWorldJournal 2015:1–15.  https://doi.org/10.1155/2015/945689 CrossRefGoogle Scholar
  102. 102.
    Ahangi A, Karamnejad M, Mohammadi N, Ebrahimpour R, Bagheri N (2012) Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Comput Appl 23:1319–1327.  https://doi.org/10.1007/s00521-012-1074-3 CrossRefGoogle Scholar
  103. 103.
    Polat K, Gunes S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187:1017–1026.  https://doi.org/10.1016/j.amc.2006.09.022 CrossRefGoogle Scholar
  104. 104.
    Rzempoluck EJ (2012) Neural network data analysis using SimulnetTM. Springer Science & Business MediaGoogle Scholar
  105. 105.
    Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: 14th international joint conference on artificial intelligence. Morgan Kaufmann Publishers, Montreal, pp 1137–1143Google Scholar
  106. 106.
    Guo L, Rivero D, Dorado J, Rabuñal JR, Pazos A (2010) Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J Neurosci Methods 191:101–109.  https://doi.org/10.1016/j.jneumeth.2010.05.020 CrossRefPubMedGoogle Scholar
  107. 107.
    Übeyli ED (2010) Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Syst Appl 37:233–239.  https://doi.org/10.1016/j.eswa.2009.05.012 CrossRefGoogle Scholar
  108. 108.
    Wang D, Miao D, Xie C (2011) Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl 38:14314–14320.  https://doi.org/10.1016/j.eswa.2011.05.096 CrossRefGoogle Scholar
  109. 109.
    Du X, Dua S, Acharya RU, Chua CK (2012) Classification of epilepsy using high-order spectra features and principle component analysis. J Med Syst 36:1731–1743.  https://doi.org/10.1007/s10916-010-9633-6 CrossRefPubMedGoogle Scholar
  110. 110.
    Xie S, Krishnan S (2013) Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis. Med Biol Eng Comput 51:49–60.  https://doi.org/10.1007/s11517-012-0967-8 CrossRefPubMedGoogle Scholar
  111. 111.
    Fu K, Qu J, Chai Y, Dong Y (2014) Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomed Signal Process Control 13:15–22.  https://doi.org/10.1016/j.bspc.2014.03.007 CrossRefGoogle Scholar
  112. 112.
    Lee SH, Lim JS, Kim JK, Yang J, Lee Y (2014) Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Comput Methods Prog Biomed 116:10–25.  https://doi.org/10.1016/j.cmpb.2014.04.012 CrossRefGoogle Scholar
  113. 113.
    Joshi V, Pachori RB, Vijesh A (2014) Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control 9:1–5.  https://doi.org/10.1016/j.bspc.2013.08.006 CrossRefGoogle Scholar
  114. 114.
    Xiang J, Li C, Li H, Cao R, Wang B, Han X, Chen J (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18–25.  https://doi.org/10.1016/j.jneumeth.2015.01.015 CrossRefPubMedGoogle Scholar
  115. 115.
    Peker M, Sen B, Delen D (2016) A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J Biomed Heal Inf 20:108–118.  https://doi.org/10.1109/JBHI.2014.2387795 CrossRefGoogle Scholar
  116. 116.
    Tawfik NS, Youssef SM, Kholief M (2016) A hybrid automated detection of epileptic seizures in EEG records. Comput Electr Eng 53:177–190.  https://doi.org/10.1016/j.compeleceng.2015.09.001 CrossRefGoogle Scholar
  117. 117.
    Swami P, Gandhi TK, Panigrahi BK, Tripathi M, Anand S (2016) A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst Appl 56:116–130.  https://doi.org/10.1016/j.eswa.2016.02.040 CrossRefGoogle Scholar
  118. 118.
    Guo Y, Zhang Y, Mursalin M et al (2017) Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing 241:204–214.  https://doi.org/10.1016/j.neucom.2017.02.053 CrossRefGoogle Scholar
  119. 119.
    Sharma M, Pachori RB, Rajendra Acharya U (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 94:172–179.  https://doi.org/10.1016/j.patrec.2017.03.023 CrossRefGoogle Scholar
  120. 120.
    Subasi A, Kevric J, Abdullah Canbaz M (2017) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl:1–9.  https://doi.org/10.1007/s00521-017-3003-y
  121. 121.
    Li Y, Cui W, Luo M, Li K, Wang L (2018) Epileptic seizure detection based on time-frequency images of EEG signals using Gaussian mixture model and gray level co-occurrence matrix features. Int J Neural Syst 28:1850003.  https://doi.org/10.1142/S012906571850003X CrossRefPubMedGoogle Scholar
  122. 122.
    Hussain L, Saeed S, Awan IA, Idris A (2018) Multiscaled complexity analysis of EEG epileptic seizure using entropy-based techniques. Arch Neurosci 5:1–11.  https://doi.org/10.5812/archneurosci.61161 CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceBhai Sangat Singh Khalsa CollegeBangaIndia
  2. 2.Department of Computer Science and EngineeringSant Longowal Institute of Engineering and TechnologyLongowalIndia
  3. 3.Department of Electrical and Instrumentation EngineeringSant Longowal Institute of Engineering and TechnologyLongowalIndia

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