Volume 30 Issue 3
May  2021
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GONG Chen, LIU Jiahui, NIU Yunyun. Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier[J]. Chinese Journal of Electronics, 2021, 30(3): 419-425. doi: 10.1049/cje.2021.03.005
Citation: GONG Chen, LIU Jiahui, NIU Yunyun. Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier[J]. Chinese Journal of Electronics, 2021, 30(3): 419-425. doi: 10.1049/cje.2021.03.005

Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier

doi: 10.1049/cje.2021.03.005

This work is supported by the National Natural Science Foundation of China (No.61872325), the Fundamental Research Funds for the Central Universities (No.2652019028), and the China Scholarship Council (No.201806405004).

  • Received Date: 2020-09-13
  • Automatic identification of intracranial electroencephalogram (iEEG) signals has become more and more important in the field of medical diagnostics. In this paper, an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of iEEG epileptic seizures. Four kinds of entropy, Sample entropy, Approximate entropy, Shannon entropy, Log energy entropy are extracted from the database as the feature vectors of Neural network (NN) during the identification process. Four kinds of classification tasks, namely Pre-ictal v Post-ictal (CD), Pre-ictal v Epileptic (CE), Post-ictal v Epileptic (DE), Pre-ictal v Post-ictal v Epileptic (CDE), are used to test the effect of our classification method. The experimental results show that our algorithm achieves higher performance in all tasks than previous algorithms. The effect of hidden layer nodes number is investigated by a constructive approach named growth method. We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD, CE, DE, and the multitask classification problem CDE, respectively.
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  • Fisher. R. S, Boas. W. V. E, Blume. W, et al., “Epileptic seizures and epilepsy: Definitions proposed by the International league against epilepsy (ILAE) and the International Bureau for Epilepsy (IBE)”, Epilepsia, Vol.46, No.4, pp.470–472, 2005.
    Duncan. J. S, Sander. J. W, Sisodiya. S. M, et al., “Adult epilepsy”, The Lancet, Vol.367, No.9516, pp.1087–1100, 2006.
    Andrzejak. R. G, Lehnertz. K, Mormann. F, et al., “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, pp.061907, 2001.
    Janjarasjitt. S, “Spectral exponent characteristics of intracranial EEGs for epileptic seizure classification”, IRBM, Vol.36, No.1, pp.33–39, 2015.
    Theeranaew. W, McDonald. J, Zonjy. B, et al., “Automated detection of postictal generalized EEG suppression”, IEEE Transactions on Biomedical Engineering, Vol.65, No.2, pp.371–377, 2017.
    Yoo. J, Yan. L, El-Damak. D, et al., “An 8-channel scalable EEG acquisition SoC with patient-specific seizure classification and recording processor”, IEEE Journal of Solid-state Circuits, Vol.48, No.1, pp.214–228, 2012.
    Gigola. S, Ortiz. F, D’attellis. C. E, et al., “Prediction of epileptic seizures using accumulated energy in a multiresolution framework”, Journal of Neuroscience Methods, Vol.138, No.1-2, pp.107–111, 2004.
    Kumar. Y, Dewal. M. L and Anand. R. S, “Relative wavelet energy and wavelet entropy based epileptic brain signals classification”, Biomedical Engineering Letters, Vol.2, No.3, pp.147–157, 2012.
    Acharya. U. R, Sree. S. V, Alvin. A. P. C, et al., “Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework”, Expert Systems with Applications, Vol.39, No.10, pp.9072–9078, 2012.
    Raghu. S and Sriraam. N, “Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures”, Expert Systems With Applications, Vol.89, pp.1087–1100, 2006.
    Gao. J. F, Hui. S. I, Bin. Y. U, et al., “Lie detection analysis based on the sample entropy of eeg”, Acta Electronica Sinica, Vol.45, No.8, pp.1836–1841, 2017. (in Chinese)
    Huang. J. R, Fan. S. Z, Abbod. M, et al., “Application of multivariate empirical mode decomposition and sample entropy in EEG signals via artificial neural networks for interpreting depth of anesthesia”, Entropy, Vol.15, No.9, pp.3325–3339, 2013.
    Nicolaou. N. and Georgiou. J., “Detection of epileptic electroencephalogram based on permutation entropy and support vector machines”, Expert Systems with Applications, Vol.39, No.1, pp.202–209, 2012.
    Kumar. Y., Dewal. M. L., and. R. S., “Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network”, Signal, Image and Video Processing, Vol.8, No.7, pp.1323–1334, 2014.
    Srinivasan. V., Eswaran. C., Sriraam. N., et al., “Approximate entropy-based epileptic EEG detection using artificial neural networks”, IEEE Transactions on Information Technology in Biomedicine, Vol.11, No.3, pp.288–295, 2007.
    YIN Yi and SHANG Pengjian, “Multivariate multiscale sample entropy of traffic time series”, Nonlinear Dynamics, Vol.86, No.1, pp.479–488, 2016.
    Kannathal. N, Min. L. C, et al., “Entropies for detection of epilepsy in EEG”, Computer methods and programs in biomedicine, Vol.80, No.3, pp.187–194, 2005.
    Kumar. S. P, Sriraam. N, Benakop. P. G, et al., “Entropies based detection of epileptic seizures with artificial neural network classifiers”, Expert Systems with Applications, Vol.37, No.4, pp.3284–3291, 2010.
    Ling. G, Rivero. D, Seoane. J. A, et al., “Classification of EEG signals using relative wavelet energy and artificial neural networks”, Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, Shanghai, China, pp.177–184, 2009.
    Aydın. S, Saraoğlu, et al., “Log energy entropy-based EEG classification with multilayer neural networks in seizure”, Annals of Biomedical Engineering, Vol.37, No.12, pp.2626, 2009.
    Curteanu. S and Cartwright. H, “Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks”, Journal of Chemometrics, Vol.25, No.10, pp.527–549, 2011.
    Sun. J, “Learning algorithm and hidden node selection scheme for local coupled feedforward neural network classifier”, Neurocomputing, Vol.79,pp.158–163, 2012.
    Islam. M. M, Yao. X and Murase. K, “A constructive algorithm for training cooperative neural network ensembles”, IEEE Transactions on Neural Networks, Vol.14, No.4, pp.820–834, 2003.
    ZHANG Shirui and LI Xinke, “An estimation algorithm of BP neural network hidden layer nodes based on simulated annealing”, Journal of Hefei University of Technology (Natural Science), No.11, pp.10, 2017. (in Chinese).
    ZHANG Xuejun, HUO Yan and WAN Dongsheng, “Improved EMD based on piecewise cubic hermite interpolation and mirror extension”, Chinese Journal of Electronics, Vol.29, No.5, pp.899–905, 2020.
    WANG Danyang and SHAO Fangming, “Research of neural network structural optimization based on information entropy”, Chinese Journal of Electronics, Vol.29, No.4, pp.632–638, 2020.
    Shannon. C. E, “A mathematical theory of communication”, Bell System Technical Journal, Vol.27, No.3, pp.379–423, 1948.
    Pincu. S, “Approximate entropy (ApEn) as a complexity measure”, Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol.5, No.1, pp.110–117, 1995.
    Richman. J. S and Moorman. J. R, “Physiological time-series analysis using approximate entropy and sample entropy”, American Journal of Physiology-Heart and Circulatory Physiology, Vol.278, No.6, pp.H2039–H2049, 2000.
    Yao. W, Hu. H, Wang. J, et al., “Multiscale ApEn and SampEn in quantifying nonlinear complexity of depressed MEG”, Chinese Journal of Electronics, Vol.28, No.4, pp.817–821, 2019.
    Woodbury. George, “An introduction to statistics”, Cengage Learning, 2001.
    Sharmila. A and Geethanjali. P, “DWT based detection of epileptic seizure from EEG signals using naive Bayes and kNN classifiers”, IEEE Access, Vol.4, pp.7716–7727, 2016.
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