癲癇是一種最常見的神經系統失調疾病之一，全球約有1%的人患有癲癇，其中25%的癲癇患者無法經由任何方式成功治療。癲癇的發作是由大腦的不正常放電所引起，因此在臨床評估、偵測、及治療癲癇上，腦電圖已成為極重要的工具。假使一個可靠且自動的癲癇偵測系統被開發，將可降低專家判讀腦波癲癇診斷所需的時間，同時此系統如進一步發展為線上警告醫護人員或應用在電刺激系統等治療裝置上，將可提升病人的日常生活安全與品質。

在此篇論文中，我們發展一套可靠且運算快速的階層式癲癇偵測系統，可以在病人癲癇發作短時間內即時偵測到。首先，我們利用獨立成分分析法從多通道訊號去除雜訊並取得一個包含癲癇訊號的成份，接著經由頻譜、複雜度、線段長度與hjorth等方法分析正常、雜訊與癲癇腦波訊號，從中找出可以有效區分的特徵點。分類部分在第一層使用線段長度與mdc參數，分別使用適當的閥值偵測癲癇，將正常與雜訊訊號過濾掉。第二層使用ApEn當複雜度的基準及頻譜的頻帶能量結合線性分類器來做最後癲癇偵測的判斷。此方法已實際應用在十一位癲癇病人上，每位病人測試的腦波長度平均為連續22.3小時，同時，我們也用其他三種已發表的偵測方法應用在此論文所使用的腦波訊號上並比較偵測結果，實驗結果顯示我們提出的階層式即時偵測系統癲癇偵測率可達到95.24%，比其他偵測方法具有較低的誤判率，平均誤判率不超過0.09次數/小時，較短的癲癇偵測時間，平均可於發作9.2秒內偵測到。

Epilepsy is one of the most common neurological disorders, approximately 1% of people in the world have epilepsy, and 25% of epilepsy patients cannot be treated sufficiently by any available therapy. Epilepsy is caused by abnormal discharges in the brain, thus EEG has long been an especially valuable clinical tool for the evaluation, detection, and treatment of epilepsy. If a robust and automatic seizure detection system was available, it could reduce the time required by a neurologist to perform an off-line epilepsy diagnosis by reviewing electroencephalogram (EEG) data. Furthermore, it could produce an on-line warning signal to alert healthcare professionals or to drive a treatment device such as an electrical stimulator to enhance the patient’s safety and quality of life.

In this study, we develop a robust system which computes quickly to detect immediately pathological changes of seizure in human in short time. First, the multichannel EEG signals are analyzed with the aid of Fast Independent Component Analysis (FastICA) to remove artifact and obtain one component related to the epileptic seizures. Second, we apply spectrum, complexity, line length and hjorth analysis to EEG recordings of normal, artifact and seizure to find distinguishable features. On seizure classification, two thresholds of line length and mdc are used to exclude the normal and artifact EEG out at first stage. At stage two, the ApEn and spectral subbands are combined with linear classifier for final seizure detection. The method has been implemented on 11 patients with average continuous 22.3 hour EEG recordings. We also apply the other three detection methods in referenced paper to the same dataset. Compared with the other methods, the performance shows that the hierarchical approach has several advantages including high accuracy of seizure detection which reaches to 95.24%, lower false alarm below average 0.09 FP/hr and detection delay shorter than average 9.2 seconds.

Adeli, H., Zhou, Z., and Dadmehr, N., “Analysis of EEG records in an epileptic patient using wavelet transform,” Journal of Neuroscience Methods, vol. 123, pp. 69-87, 2003.
Aschenbrenner-Scheibe, R., Maiwald, T., Winterhalder, M., Voss, Hu., Timmer, J., Schulze-Bonhage, A., “How well can epileptic seizures be predicted? An evaluation of a nonlinear method,” Brain, vol. 126, pp. 2616-2626, 2003.
Acir, N., Öztura, İ., Kuntalp, M., Baklan, B., and Güzeliş, C., “Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 1, pp. 30-40, 2005.
Alkan, A., Koklukaya, E., Subasi, A., “Automatic seizure detection in EEG using logistic regression and artificial neural network,” Journal of Neuroscience Method, vol. 148, pp. 167-176, 2005.
Adeli, H., Ghosg-Dastidar, S., and Dadmehr, N., “A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, 2007.
Aarabi, A., Fazel-Rezai, R., Aghakhani, Y., “A fuzzy rule-based system for epileptic seizure detection in intracranial EEG,” Clinical Neurophysiology, vol. 120, no. 9, pp. 1648-1657, 2009.
Tzallas, A. T., Tsipouras, M. G., and Fotiadis, D. I., “Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis,” Transactions on Information Technology in Biomedicine, Vol. 13, No. 5, September, 2009.
Bandt, C., and Pompe, B., “Permutation entropy: a natural complexity measure for time series,” Physical Review Letters, vol. 88, 2002.
Boon, P., Vonck, K., De Herdt V. et al., “Deep brain stimulation in patients with refractory temporal lobe epilepsy, ” Epilepsia, vol. 48, pp. 1551-1560, 2007.
Bosnyakova, D., Gabova, A., Zharikova, A., Gnezditski, V., Kuznetsova, G., and van Luijtelaar, G., “Some peculiarities of time-frequency dynamics of spike-wave discharges in humans and rats,” Clinical Neurophysiology, vol. 118, pp. 1736-1743, 2007.
Chabardes, S., Kahane, P., Minotti, L., Koudsie, A., Hirsch, E., Benabid, A. L., “Deep brain stimulation in epilepsy with particular reference to the subthalamic nucleus,” Epileptic Disorders, vol. 4, no. 3, pp. 83-93, 2002.
Chaovalitwongse, W. A., Fan, Y., and Sachdeo, E.C. “On the time series k-nearest neighbor classification of abnormal brain activity,” IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, vol. 37, no. 6, 2007.
Duda, R. O., Hart, P. E., and Stork, D. G. “Pattern Recognition,” 2nd edition, Wiley-Interscience, New York, 2001.
Esteller, R., Echauz, J., Tcheng, T., Litt, B., “Line length: An efficient feature for seizure onset detection,” 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, October, 2001.
Esteller, R., Echauz, J., Tcheng, T., “Comparison of line length feature before and after brain electrical stimulation in epileptic patients,” Engineering in medicine and biology society, vol. 2, pp. 4710-4713, 2004.
Franaszczuk, P. J., Blinowska, K. J., Kowalczyk, M., “The application of parametric multichannel spectral estimates in the study of electrical brain activity,” Biological cybernetics, vol. 51, pp. 239-247, 1985.
Fukunaga, K., “Statistical Pattern Recognition ,” 2nd edition, Academic Press, New York, 1990.
Firpi, H., Goodman, E. D., and Echauz, J., “Epileptic seizure detection using genetically programmed artificial features,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, pp. 212-224, 2007.
Grassberger, P., Procaccia, I., “Estimation of the Kolmogorov entropy from a chaotic signal,” Physical Review, vol. 28, pp. 2591-2593, 1983.
Gevins, A. S., Remond, A. R., “Handbook of electroencephalography and clinical neurophysiology,” Amsterdam: Elsevier, 1987.
Gotman, J., Flanagan, D., Zhang, J., Rosenblatt, B., “Automatic seizure detection in the newborn: methods and initial evaluation,” Electroencephalography and clinical Neurophysiology, vol. 103, pp. 356-362, 1997.
Güler, N. F., Übeyli, E. D., Güler, I., “Recurrent neural networks employing Lyapunov exponents for EEG signals classification,” Expert Systems with Applications, vol. 29, no. 3, pp. 506–514, 2005.
Gardner, A. B., Krieger, A. M., Vachtsevanos, G., and Litt, B., “One-class novelty detection for seizure analysis from intracranial EEG,” Journal of Machine Learning Research, vol. 7, pp. 1025–1044, 2006.
Ghosh-Dastidar, S., Adeli, H., and Dadmehr, N., “Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 2, 2008.
Hjorth, B., “EEG analysis based on time domain properties,” Electroencephalography and Clinical Neurophysiology, vol. 29, pp. 306-310, 1970.
Hyvärinen, A., “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, May, 1999.
Hese, P. V., Martens, J-P, Boon, P., Dedeurwaerdere, S., Lemahieu, I., and Van de Walle, R., “Detection of spike and wave discharges in the cortical EEG of genetic absence epilepsy rats from Strasbourg,” Physics in Medicine and Biology, vol. 48, pp. 1685-1700, 2003.
Iasemidis, L. D., and Sackellares, J. C., “Chaos theory and epilepsy,” Neuroscientist, vol. 2, no. 2, pp. 118–126, 1996.
Iasemidis, L. D., Shiau, D. S., Pardalos, P. M., Chaovalitwongse, W., Narayanan, K., Prasad, A., Tsakalis, K., Carney, P. R., Sackellares, J. C., “Long-term prospective on-line real-time seizure prediction,” Clinical Neurophysiology, vol. 116, pp. 532-544, 2005.
Indiradevi, K. P., Elias, E., Sathidevi, P. S., “Complexity analysis of electroencephalogram records of epileptic patients using Hurst exponent,” International Journal of Medical Engineering and Informatics, vol. 1, no. 3, pp. 368-380, 2009.
Jung, T. P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., and Sejnowski, T., “Removal of eye activity artifacts form visual event-related potential in normal and clinical subjects,” Clinical Neurophysiology, vol. 111, pp: 1745–1758, 2000.
James, C. J., and Hesse, C. W., “Independent Component Analysis and Blind Signal Separation,” Springer Berlin / Heidelberg, LNCS 3195, pp. 1025-1032, 2004.
Katz, M., “Fractals and the analysis of waveforms,” Computers in biology and medicine, vol. 18, pp. 145-156, 1988.
Kossoff, E. H., Ritzl, E. K., Politsky, J. M., Murro, A. M., Smith, J. R., Duckrow, R. B., Spencer, D. D., and Bergey, G. K., “Effect on external responsive neurostimulator on seizures and electrographic discharges during subdural electrode monitoring,” Epilepsia, vol. 45, no. 12, pp. 1560-1567, 2004.
Kannathal, N., Acharya, U. R., Lim, C. M., and Sadasivan, P. K., “Characterization of EEG—a comparative study,” Computer Methods and Programs in Biomedicine, vol. 80, no. 1, pp.17–23, 2005.
Kannathal, N., Choo, M. L., Acharya, U. R., and Sadasivan, P. K., “Entropies for detection of epilepsy in EEG,” Computer Method and Programs in Biomedicine, vol. 80, pp. 187-194, 2005.
Kocyigit, Y., Alkan, A., Erol, H., “Classification of EEG recordings by using fast independent component analysis and artificial neural network,” Journal of Medical Systems, vol. 32, no. 1, pp. 17-20, 2008.
Labar, D., Murphy, J., and Tecoma, E., “Vagus nerve stimulation for medication-resistant generalized epilepsy,” Neurology, vol. 52, no. 7, pp. 1510–12, 1999.
Leppik, I. E. “Contemporary diagnosis and management of the patient with epilepsy,” Handbooks in health care, Newton, Pennsylvania, USA, fifth edition, 2000.
Litt, B., “Evaluating devices for treating epilepsy,” Epilepsia, vol. 44(Suppl. 7), pp. 30-37, 2003
Li, X., Ouyang, G., and Richards, D. A., “Predictability analysis of absence seizures with permutation entropy,” Epilepsy Research, vol. 77, pp. 70-74, 2007.
Liang, S. F., Wang, H. C., Liao, Y. C., “An automatic epileptic seizure detection system and its implementation on an embedded system,” Proceeding of 13th International Conference on Human-Computer Interaction, San Diego, USA, July, Posters pp. 711-715, 2009.
Liang, S. F., Wang, H. C., and Chang, W. L., “Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection,” EURASIP Journal on Advances in Signal Processing, vol. 2010, 2010.
Olsen, D., Lesser, R., Harris, J., Webber, R., and Cristion, J., “Automatic detection of seizures using electroencephalographic signals,” U.S. Patent 5 311 876, 1994.
Osorio, I., Frei, M. G., Sunderam, S., Giftakis, J., Bhavaraju, N. C., Schaffner, S. F., and Wilkinson, S. B., “Automated seizure abatement in human using electrical stimulation,” Annals of Neurology, vol. 57, pp. 258-268, 2005.
Ocak, H., “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Expert System with Applications, vol. 36, no. 2, pp. 2027-2036, 2009.
Pincus, S. M., “Approximate entropy as a measure of system complexity,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 88, No. 6, pp. 2297-2301, 1991.
Päivinen, N., Lammi, S., Pitkanen, A., Nissinen, J., Penttonen, M., and Gronfors, T., “Epileptic seizure detection: a nonlinear viewpoint,” Computer Methods and Programs in Biomedicine, vol. 79, no. 2, pp. 151–159, 2005.
Polat, K., and Güneş, S., “Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals,” Expert Systems with Applications, vol. 34, pp. 2039-2048, 2008.
Qu, H., and Gotman, J., “A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device, ” IEEE Transactions on Biomedical Engineering, vol. 44, no. 2, pp. 115–122, 1997.
Richman, J. S., and Moorman, J. R., “Physiological time-series analysis using approximate and sample entropy,” American Journal of Physiology - Heart and Circulatory Physiology, vol. 278, pp. H2039-H2049, 2000.
Saab, M. E., and Gotman, J., “A system to detect the onset of epileptic seizures in scalp EEG,” Clinical Neurophysiology, vol. 116, pp. 427-442, 2005.
Schelter, B., Winterhalder, M., Maiwald, T., Brandt, A., Schad, A., Schulze-Bonhage, A., Timmer, J., “Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction,” Chaos, vol. 16, pp. 013108, 2006.
Schuyler, R., White, A., Staley, K., and Cios, K. J., “Epileptic seizure detection,” IEEE Engineering in Medicine and Biology Magazine, pp. 74-81, 2007.
Srinivasan, V., Eswaran, C., and Sriraam, N., “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.
Tzallas, A. T., Tsipouras, M. G, and Fotiadis, D. I., “Epileptic Seizure Detection in EEGs Using Time—Frequency Analysis,” Transactions on Information Technology in Biomedicine, Vol. 13, No. 5, September, 2009.