Abstract
Epilepsy is a serious brain disorder affecting nearly 1 % of the world’s population. Detecting the epileptic seizures in EEGs is not only the first step for the diagnosis, but also a significant evidence for the treatment follow-up in epilepsy patients. In recent years, automatic seizure detection using epileptic EEGs has been developed with the significance of relieving the heavy workload of traditional visual inspection for diagnosing epilepsy. The appropriate feature extraction method and efficient classifier are recognized to be crucial in the successful realization. This paper first designs a novel lagged-Poincaŕe-based feature extraction method on the basis of a class of lagged Poincaŕe plots, as well as the scatter-degree and distribution-uniformity of them which are explored to characterize the lag-T Poincaŕe plots from the quantitative point of view. Then we propose an automatic seizure detection method LPBF-ELM which integrates the lagged-Poincaŕe-based feature LPBF and extreme learning machine (ELM). Experimental results on Bonn database demonstrate that the proposed method LPBF-ELM does a good job in epileptic seizure detection while preserving the efficiency and simplicity.
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Acharya, U. R., Molinari, F., Subbhuraam, V. S., & Chattopadhyay, S. (2012). Automated diagnosis of epileptic EEG using entropies. Biomedical Signal Processing and Control, 7, 401–408.
Altunay, S., Telatar, Z., & Erogul, O. (2010). Epileptic eeg detection using the linear prediction error energy. Expert Systems with Applications, 37, 5661–5665.
Brignol, A., Al-Ani, T., & Drouot, X. (2013). Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: A comparative study using short and standard epoch lengths. Computer Methods and Programs in Biomedicine, 109, 227–238.
Chandaka, S., Chatterjee, A., & Munshi, S. (2009). Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Systems with Applications, 36, 1329–1336.
Fernandez-Blanco, E., Rivero, D., Rabunal, J., Dorado, J., Pazos, A., & Munteanu, C. R. (2012). Automatic seizure detection based on star graph topological indices. Journal of Neuroscience Methods, 209, 410–419.
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. Biomedical Siganl Processing and Control, 13, 15–22.
Fu, K., Qu, J., Chai, Y., & Zou, T. (2015). Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals. Biomedical Signal Processing and Control, 18, 179–185.
Gotman, J. (1982). Automatic recognition of epileptic seizures in the EEG. Electroencephalography and Clinical Neurophysiology, 54(5), 530–540.
Guo, L., Rivero, D., Dorado, J., Rabunal, J. R., & Pazos, A. (2010). Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural network. Journal of Neuroscience Methods, 191, 101–109.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.
Iscan, Z., Dokur, Z., & Demiralp, T. (2011). Classification of electroencephalogram signals with combined time and frequency features. Expert Systems with Applications, 38(8), 10 499–10 505.
Joshi, V., Pachoria, R. B., & Vijesh, A. (2014). Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomedical Signal Processing and Control, 9, 1–5.
Kumar, Y., Dewal, M. L., & Anand, R. S. (2014). Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing, 133, 271–279.
Kumar, T. S., Kanhangad, V., & Pachori, R. B. (2015). Classification of seizure and seizure-free EEG signals using local binary patterns. Biomedical Signal Processing and Control, 15, 33C40.
Li, S., Zhou, W., Yuan, Q., Geng, S., & Cai, D. (2013). Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in Biology and Medicine, 43, 807–816.
Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R. (2009). Classification of patterns of EEG synchronization for seizure prediction, 120(11), 1927–1940.
Murro, A. M., King, D. W., Smith, J. R., Gallagher, B. B., Flanigin, H. F., & Meador, K. (1991). Computerized seizure detection of complex partial seizures. Electroencephalography and Clinical Neurophysiology, 79(4), 330–333.
Naghsh-Nilchi, A. R., & Aghashahi, M. (2010). Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network. Biomedical Signal Processing and Control, 5, 147–157.
Nicolaou, N., & Georgiou, J. (2012). Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Systems with Applications, 39, 202–209.
Niknazar, M., Mousavi, S.R., Vahdat, B.V., Shamsollahi, M.B., & Sayyah, M. (2010). A new dissimilarity index of EEG signals for epileptic seizure detection. In International Symposium on Communications, Control and Signal Processing, pp. 1–5.
Orhan, U., Hekim, M., & Ozer, M. (2011). EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems with Applications, 38, 13 475–13 481.
Sharma, R. R. (2014). Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems with Applications, 42, 1106–1117.
Siuly, W., & Li, Y. (2011). Clustering technique-based least square support vector machine for EEG signal classification. Computer Methods and Programs in Biomedicine, 104, 358–372.
Song, Y., Crowcroft, J., & Zhang, J. (2012). Automated epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. Journal of Neuroscience Methods, 210, 132–146.
Subasi, A., Erelebi, E., Alkan, A., & Koklukaya, E. (2006). Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection. Computers in Biology and Medicine, 36, 195–208.
Tito, M., Cabrerizo, M., Ayala, M., Barreto, A., Miller, I., Jayakar, P., et al. (2009). Classification of electroencephalographic seizure recordings into ictal and interictal files using correlation sum. Computers in Biology and Medicine, 39, 604–614.
van Mierlo, P., Papadopoulou, M., Carrette, E., Boon, P., Vandenberghe, S., Vonck, K., et al. (2014). Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization. Progress in Neurobiology, 121, 19–35.
Yuan, Q., Zhou, W., Li, S., & Cai, D. (2011). Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Research, 96, 29–38.
Zhu, G., Li, Y., & Wen, P. (2014). Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm. Computer Methods and Programs in Biomedicine, 115(2), 64–75.
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Song, JL., Zhang, R. Application of extreme learning machine to epileptic seizure detection based on lagged Poincaŕe plots. Multidim Syst Sign Process 28, 945–959 (2017). https://doi.org/10.1007/s11045-016-0419-y
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DOI: https://doi.org/10.1007/s11045-016-0419-y