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研究生:張琬琳
研究生(外文):Wan-lin Chang
論文名稱:基於遞迴式神經網路之癲癇類型診斷及癲癇發作偵測系統
論文名稱(外文):Multi-type Epilepsy Diagnosis and Automatic Epileptic Seizure Detection Based on Recurrent Neural Networks
指導教授:梁勝富梁勝富引用關係
指導教授(外文):Sheng-fu Liang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:80
中文關鍵詞:複雜度分類腦電圖癲癇發作偵測癲癇頻譜
外文關鍵詞:complexityclassification.seizure detectionentropyspectrumelectroencephalogram (EEG)Epilepsy
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  • 被引用被引用:2
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  • 收藏至我的研究室書目清單書目收藏:1
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%的癲癇患者不能經由任何方式成功治療。癲癇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,腦電圖已是極為重要的工具。近年來已開發出許多藉由腦電圖來控制釋放藥物或給予電刺激來抑制癲癇發作的裝置且在臨床下運作。然而,運算快速並能立即根據各種癲癇種類在人類病理上的變化而作用的裝置目前則尚待開發。
在這篇論文中,在及時癲癇偵測上,我們經由頻譜和複雜度的分析,提出了一個可靠的癲癇偵測方法。我們利用ApEn來當複雜度的基準及頻譜的頻帶能量來做為癲癇偵測的方法。我們提出一個適當的閥值來減少誤判,並選擇遞迴式神經網路來使之能在癲癇發作後極短時間內偵測到以便給予抑制。此方法已應用在兩種不同種類的癲癇上,我們提出偵測方法有三大優點,第一,較高的癲癇偵測率(可以達到100%)。第二,較低的誤判率(誤判不超過2.5%),第三,癲癇可於發作0.5秒內偵測到。
Epilepsy is one of the most common neurological disorders, approximately 1% of people in the world have epilepsy, 25% of epilepsy patients cannot be treated sufficiently by any available therapy. Epilepsy is caused by abnormal discharges in the brain, thus EEG has been an especially valuable clinical tool for the evaluation, detection, and treatment of epilepsy. Through EEG recordings, a number of systems which can release drug or give an electrical stimulation to suppress the seizures have been developed and under clinical operation for years. However, a robust device has not yet been developed which compute quickly and fast enough to action to meet immediately pathological changes of different types of seizures in human.
In this study, we research multi-type epilepsy diagnosis that can be applied to control the multi-type epilepsy with different method. We present a three-type epileptic diagnosis method, with using permutation entropy as the complexity index and spectrum band power with RBFSVM classify. The average accuracies of the RBFSVM reach to 79%. The detection rates of temporal EEGs can reach 97.6%. However, if we distinguish temporal EEGs (Set C) and non-temporal EEGs (Set A and B). The average accuracy can reach higher than 97% with RBFSVM. The classification results can be utilized to a system to determine what type of epilepsy when patients have mixed epilepsy.
In the on-line detection, the results of the feasibility of developing algorithms to detect seizures based on automated analysis of the spatiotemporal dynamical characteristics of EEG recordings. We present a reliable epileptic seizures detection method, with using approximate entropy as the complexity index and spectrum band power. We present an adaptive threshold method that reduced false alarm. We also selecting recurrent neural network to be classifier which can detect seizures in a short time while seizures onset. The method has tested on three types of seizures including long-term recordings, robustly. This method was shown with several aspects of advantages, including high accuracy of on-line seizure detection (reach 100%), low false alarm (below 2.5%). The seizure detection latency was not greater than 0.5 sec after seizure onset.
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Seizure Types 2
1.2.1 Partial Seizures 3
1.2.2 Generalized Seizures 4
1.3 Related Work: Seizure Detection 6
1.4 Study Objective 8
1.5 Thesis Organization 8
Chapter 2 On-line Seizure Detection 10
2.1 Seizure Types 11
2.1.1 Absence Seizure 11
2.1.2 PTZ Induced Seizure 13
2.2 Animal Experiment 13
2.3 EEG Data Acquisition 16
2.4 Feature Extraction 19
2.4.1 Complexity Analysis 19
2.4.2 Spectrum Analysis 30
2.5 Classification 34
2.5.1 Classifiers 35
2.5.2 Adaptive Thresholding 42
2.5.3 Seizure Detection 43
2.6 Performance Evaluation 44
2.7 Result 45
2.8 Discussion 54
Chapter 3 Conclusion and Future Work 61
3.1 Conclusion 61
3.2 Future Work 61
Reference 62
Appendix A Seizure Diagnosis 67
A. 1 Temporal Lobe Epilepsy 67
A. 2 Datasets 69
A. 3 Feature Extraction 73
A. 4 Result 77
A. 5 Discussion 80
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.
Acır, 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.
Acir, N., “A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems,” Expert Systems with Applications, vol. 31, no. 1, pp. 150-158, 2006.
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.
Bandt, C. and Pompe, B., “Permutation entropy: a natural complexity measure for time series,” Physical Review Letters, vol. 88, no. 17, 2002
Blumenfeld, H., “Consciousness and epilepsy: why are the patients with absence seizures absent?” Progress in brain research, vol.150, pp. 271-86, 2005.
Bosnyakova, D., Gabova, A., Zharikova, A., Gnezditski, V., Kuznetsova, G., and van Luijtelaar, F., “Some peculiarities of time-frequency dynamics of spike-wave discharges in humans and rats,” Clinical Neurophysiology, vol. 118, pp. 1736-1743, 2007.
Crunelli, V. and Leresche, N., “Childhood absence epilepsy: genes, channels, neurons and networks,” Nature Reviews Neuroscience, vol. 3, no.3, pp. 71-82, 2002.
Coenen AML, van Luijtelaar ELJM, “Genetic animal models for absence epilepsy: a review of the WAG/Rij strain of rats,” Behavior genetics, vol. 33, no. 6, pp. 35-53, 2003.
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.
Drinkenburg, W., van Luijtelaar, E., van Schaik, W. J. and Coenen, A., “Aberrant transients in the EEG of epileptic rats: a spectral analytical approach,” Physiology & behavior, vol. 54, no. 7, pp. 79–83, 1993.
Danober, L., Deransart, C., Depaulis, A., Vergnes, M., and Marescaus, C., “Pathophysiological mechanisms of genetic absence epilepsy in the rat,” Progress in Neurobiology, vol. 55, pp.27-57, 1998.
Depaulis, A., van Luijtelaar, G., Genetic model of absence epilepsy in rat. In: Pitkanen, A., Schwartzkroin, P., Moshe, S., editors. Models of seizures and epilepsy. San Diego: Elsevier, vol.3, pp. 33–43, 2006
Edward, H. B., “Temporal lobe epilepsy: Where do the seizure really begin?” Epilepsy & Behavior, vol.14, pp.32-37, 2009.
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.
Golub, G., “Numerical methods for solving linear least squares problems,” Numerische Mathematik, vol. 7, pp. 206-216, 1965.
Gloor, P., Quesney, L.F. and Zumstein, H., “Pathophysiology of generalized penicillin epilepsy in the cats: the role of cortical and subcortical structures. II. Topical application of penicillin to the cerebral cortex and subcortical structures,” Electroencephalography and clinical neurophysiology, vol.43, pp. 79-94, 1977.
Ghosh-Dastidat, S., Adeli, H., and Dadmehr, N., “Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 9, pp. 1545-1551, 2007.
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.
Iasemidis, L.D., Shiau, D. S., Pardalos, P.M., Chaovalitwongse, W., Narayanan, K., Prasad, A. Tsakalis, K., Carney, P.R. and Sackellares, J.C.,”Long-term prospective on-line real-time seizure prediction,” Clinical Neurophysiology, vol. 116, pp. 532-544, 2005.
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., Choo, M. L., Rajendra Acharya, U., and Sadasivan, P. K., “Entropies for detection of epilepsy in EEG,” Computer Method and Programs in Biomedicine, vol. 80, pp. 187-194, 2005.
Leppik, I.E., “Contemporary diagnosis and management of the patient with epilepsy,” Handbooks in health care, Newton, Pennsylvania, USA, fifth edition, 2000.
Litt, B. and Echauz, J., “Prediction of epileptic seizures,” Lancet Neurology, vol. 1, no. 1, pp. 22–30, 2002.
Lehnertz, K., Mormann, F., Kreuz, T. et al., “Seizure prediction by nonlinear EEG analysis,” IEEE Engineering in Medicine and Biology Magazine, vol. 22, no. 1, pp. 57–63, 2003.
Litt, B., “Evaluating devices for treating epilepsy,” Epilepsia, vol. 44(Suppl. 7), pp. 30-37, 2003.
Lin., C.T., Wu, R. C., Liang, S.-F., Chao, W.-H., Chen, Y.-J., and Jung, T.-P., “EEG-based drowsiness estimation for safety driving using independent component analysis,” IEEE Transactions on Circuits and Systems-I: Regular Papers, vol. 52, no. 12, pp. 2726-2737, 2005.
Li, X., Ouyang, G., and Richards, D. A., “Predictability analysis of absence seizures with permutation entropy,” Epilepsy Research, vol. 77, pp. 70-74, 2007.
Marescaux, C., Vergnes, M. and Depaulis, A., “Genetic absence epilepsy in rats from Strasburg,” Journal of neural transmission, vol. 35, pp. 37-69, 1992.
Manning, J.-P. A., Richards, D. A., and Bowery, N. G., “Pharmacology of absence epilepsy,” TRENDS in Pharmacological Sciences, vol. 24, no. 10, 2003.
Mormann, F., Andrzejak, R. G., Elger, C. E. and Lenhnertz, K.,“Seizure prediction: the long and winding road,” Brain, vol. 130, no. 2, pp. 313–333, 2006.
Niedermayer, E., “Primary (idiopathic) generalized epilepsy and underlying mechanisms,” Clinical EEG electroencephalography, vol.27, pp. 1-21, 1996.
Nordin1, F. H. and Nagi2, F.H., “Layer-recurrent network in identifying a nonlinear system” International Conference on Control, Automation and Systems, 2008.
Osorio, I., Frei, M. G., Giftakis, J., Peters, T., Ingram, J., Turnbull, M., Herzog, M., Rise, M. T., Schaffner, S., Wennberg, R. A., Walczak, T. S., Risinger, M. W., and Ajmone-Marsan, C., “Performance reassessment of a real-time seizure-detection algorithm on long ECoG series,” Epilepsia, vol. 43, no. 12, pp.1522-1535, 2002.
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, pp. 2027-2096, 2009.
Pincus, S. M., “Approximate entropy as a measure of system complexity,” Proceedings of the National Academy of Science of the United State of America, vol. 88, pp. 2291-2301, 1991.
Paul, J. S., and Patel, C. B., “Prediction of PTZ-induced seizures using wavelet-based residual entropy of cortical and subcortical field potentials,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 5, pp. 640-648, 2003.
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.
Racine, R.J., “Modification of seizure activity by electrical stimulation. II. Motor seizure,” Electroencephalography and Clinical Neurophysiology, vol. 3, no. 3, pp.81-94, 1972.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J., “Learning representations by back-propagating errors,” Nature, vol. 323, pp.533-536, 1986.
Richman, J. S., and Moorman, J. Randall Moorman, “Physiological time-series analysis using approximate and sample entropy,” American Journal of Physiology - Heart and Circulatory Physiology, vol. 278, pp. H2039-H2049, 2000.
Rosso, O. A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schürmann, M., and Basar, E., “Wavelet entropy: a new tool for analysis of short duration brain electrical signals,” Journal of Neuroscience Methods, vol. 105, pp. 65-75, 2001.
Rosso, O. A., “Entropy changes in brain function,” International Journal of Psychophysiology, vol. 64, pp. 75-80, 2007.
Shaw, F. Z., “Is spontaneous high-voltage rhythmic spike discharge in Long Evans rats and absence-like seizure activity?” Journal of Neurophysiol, vol. 91, pp.63-77, 2004.
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.
Srinivasan, V., Eswaran, C., and Sriraam, N., “Artificial neural network based epileptic detection using time-domain and frequency-domain features,” Journal of Medical Systems, vol. 29, no. 6, pp. 647-660, December, 2005.
Schuyler, R., White, A., Staley, K., and Cios, K. J., “Epileptic seizure detection,” IEEE Engineering in Medicine and Biology Magazine, pp. 74-81, 2007.
Shaw, F. Z., “7-12 Hz high-voltage rhythmic spike discharges in rats evaluated by antiepileptic drugs and flicker stimulation,” Journal of Neurophysiology, vol. 97, pp. 238-247, 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.
Vergnes, M., Marescaux, C., Micheletti, G., Reis, J., Depaulis, A. and Rumbach, L., “Spontaneous paroxysmal electroclinical pattern in rat: a model of generalized non-convulsive epilepsy,” Neuroscience, vol. 33, pp. 97-101, 1982.
van Luijtelaar ELJM, Coenen AML. “Two types of electrocortical paroxysms in an inbred strain of rats,” Neuroscience letters, vol. 70, no. 39 pp. 3-7, 1986. .
Van Hese, P., 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.
Van Hese, P. and Martens J-P, “Automatic Detection of Spike and Wave Discharges in the EEG of Genetic Absence Epilepsy Rats from Strasbourg,” IEEE Transactions on Biomedical Engineering, 2008.
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