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An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification

Published: 11 July 2015 Publication History

Abstract

A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the accuracy of predicting the onset of a seizure. Designing constructive crossover and mutation operators (CCM) and integrating local hill climbing search technique with the GP have been put forward as solutions. In this paper, we proposed a hybrid crossover and mutation operator, which uses both the standard GP and CCM-GP, to choose high performing individuals in the least possible time. To demonstrate our approach, we tested it on a benchmark EEG signal dataset. We also compared and analyzed the proposed hybrid crossover and mutation operation with the other state of art GP methods in terms of accuracy and training time. Our method has shown remarkable classification results. These results affirm the potential use of our method for accurately predicting epileptic seizures in an EEG signal and hint on the possibility of building a real time automatic seizure detection system.

References

[1]
G. Tsoumakas and I. Katakis, "Multi-label classification: An overview," Dept. of Informatics, Aristotle University of Thessaloniki, Greece, 2006.
[2]
Z. Michalewicz, Genetic algorithms
[3]
data structures= evolution programs. Springer Science & Business Media, 1996.
[4]
K. C. Tan, T. H. Lee, and E. F. Khor, "Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons," Artificial intelligence review, vol. 17, no. 4, pp. 251--290, 2002.
[5]
J. R. Koza, phGenetic Programming: vol. 1, On the programming of computers by means of natural selection. MIT press, 1992, vol. 1.
[6]
----, "Genetic evolution and co-evolution of computer programs," Artificial life II, vol. 10, pp. 603--629, 1991.
[7]
K. Yong11, "Improving crossover and mutation for adaptive genetic algorithm," Computer Engineering and Applications, vol. 12, p. 027, 2006.
[8]
U.-M. O'Reilly and F. Oppacher, "Program search with a hierarchical variable length representation: Genetic programming, simulated annealing and hill climbing," Parallel Problem Solving from Nature--PPSN III.Springer, 1994, pp. 397--406.
[9]
M. During and D. Spencer, "Extracellular hippocampal glutamate and spontaneous seizure in the conscious human brain," The lancet, vol. 341, no. 8861, pp. 1607--1610, 1993.
[10]
M. D. Bownds and D. Bownas, The biology of mind: Origins and structures of mind, brain, and consciousness. Fitzgerald Science Press Bethesda, MD, 1999.
[11]
S. Sanei and J. A. Chambers, EEG signal processing. John Wiley & Sons, 2008.
[12]
M. Teplan, "Fundamentals of eeg measurement," Measurement science review, vol. 2, no. 2, pp. 1--11, 2002.
[13]
P. Gómez-Gil, E. Juárez-Guerra, V. Alarcón-Aquino, M. Ramírez-Cortés, and J. Rangel-Magdaleno, "Identification of epilepsy seizures using multi-resolution analysis and artificial neural networks," in phRecent Advances on Hybrid Approaches for Designing Intelligent Systems. Springer, 2014, pp. 337--351.
[14]
A. Subasi, "Eeg signal classification using wavelet feature extraction and a mixture of expert model," Expert Systems with Applications, vol. 32, no. 4, pp. 1084--1093, 2007.
[15]
A. Bhardwaj, A. Tiwari, M. V. Varma, and M. R. Krishna, "Classification of eeg signals using a novel genetic programming approach," in Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion. ACM, 2014, pp. 1297--1304.
[16]
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903--995, 1998.
[17]
V. Bajaj and R. B. Pachori, "Classification of seizure and nonseizure eeg signals using empirical mode decomposition," Information Technology in Biomedicine, IEEE Transactions on, vol. 16, no. 6, pp. 1135--1142, 2012.
[18]
R. Poli and W. B. Langdon, "On the search properties of different crossover operators in genetic programming," Genetic Programming, pp. 293--301, 1998.
[19]
R. Panda, P. Khobragade, P. Jambhule, S. Jengthe, P. Pal, and T. Gandhi, "Classification of eeg signal using wavelet transform and support vector machine for epileptic seizure diction," in Systems in Medicine and Biology (ICSMB), 2010 International Conference on. IEEE, 2010, pp. 405--408.
[20]
S.-F. Liang, H.-C. Wang, and W.-L. Chang, "Combination of eeg complexity and spectral analysis for epilepsy diagnosis and seizure detection," EURASIP Journal on Advances in Signal Processing, vol. 2010, p. 62, 2010.
[21]
H. Ocak, "Optimal classification of epileptic seizures in eeg using wavelet analysis and genetic algorithm," Signal processing, vol. 88, no. 7, pp. 1858--1867, 2008.
[22]
I. Güler and E. D. Übeyli, "Adaptive neuro-fuzzy inference system for classification of eeg signals using wavelet coefficients," Journal of neuroscience methods, vol. 148, no. 2, pp. 113--121, 2005.
[23]
N. F. Güler, E. D. Übeyli, and.I. Güler, "Recurrent neural networks employing lyapunov exponents for eeg signals classification," Expert Systems with Applications, vol. 29, no. 3, pp. 506--514, 2005.
[24]
K. Aslan, H. Bozdemir, C. Şahin, S. N. Oğgulata, and R. Erol, "A radial basis function neural network model for classification of epilepsy using eeg signals," Journal of medical systems, vol. 32, no. 5, pp. 403--408, 2008.
[25]
L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, and A. Pazos, "Automatic epileptic seizure detection in eegs based on line length feature and artificial neural networks," Journal of neuroscience methods, vol. 191, no. 1, pp. 101--109, 2010.
[26]
L. Guo, D. Rivero, and A. Pazos, "Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks," Journal of neuroscience methods, vol. 193, no. 1, pp. 156--163, 2010.
[27]
K. C. Tan, Q. Yu, C. Heng, and T. H. Lee, "Evolutionary computing for knowledge discovery in medical diagnosis," Artificial Intelligence in Medicine, vol. 27, no. 2, pp. 129--154, 2003.
[28]
M. Castelli, L. Vanneschi, and S. Silva, "Semantic search-based genetic programming and the effect of intron deletion," Cybernetics, IEEE Transactions on, vol. 44, no. 1, pp. 103--113, 2014.
[29]
M. Zhang, X. Gao, and W. Lou, "A new crossover operator in genetic programming for object classification," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 37, no. 5, pp. 1332--1343, 2007.
[30]
R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,'' Physical Review E, vol. 64, no. 6, p. 061907, 2001.
[31]
A. P. Bradley, "The use of the area under the roc curve in the evaluation of machine learning algorithms,'' Pattern recognition, vol. 30, no. 7, pp. 1145--1159, 1997.

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      cover image ACM Conferences
      GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
      July 2015
      1496 pages
      ISBN:9781450334723
      DOI:10.1145/2739480
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      Publication History

      Published: 11 July 2015

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      Author Tags

      1. crossover
      2. epilepsy
      3. fitness function
      4. genetic programming
      5. hill climbing search
      6. mutation

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      GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      • (2022)Genetic Programming-Based Feature Selection for Emotion Classification Using EEG SignalJournal of Healthcare Engineering10.1155/2022/83620912022(1-6)Online publication date: 8-Mar-2022
      • (2022)Personality Prediction with Hybrid Genetic Programming using Portable EEG DeviceComputational Intelligence and Neuroscience10.1155/2022/48676302022Online publication date: 1-Jan-2022
      • (2022)Comparison of Performance of EEG-Based Depression classification2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT)10.1109/ICFEICT57213.2022.00030(125-130)Online publication date: Aug-2022
      • (2021)EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM ModelComputational Intelligence and Neuroscience10.1155/2021/65248582021Online publication date: 1-Jan-2021
      • (2021)A LSTM based deep learning network for recognizing emotions using wireless brainwave driven systemExpert Systems with Applications10.1016/j.eswa.2020.114516173(114516)Online publication date: Jul-2021
      • (2021)Epileptic Seizure Detection Using LSTM: A Deep Learning TechniqueSoft Computing for Problem Solving10.1007/978-981-16-2712-5_21(245-258)Online publication date: 14-Oct-2021
      • (2021)Personality Prediction Using EEG Signals and Machine Learning AlgorithmsSoft Computing for Problem Solving10.1007/978-981-16-2712-5_10(109-114)Online publication date: 14-Oct-2021
      • (2021)Feature Extraction for Classification Methods of EEG SignalsSoft Computing for Problem Solving10.1007/978-981-16-2709-5_29(381-392)Online publication date: 14-Oct-2021
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