دوره 15، شماره 1 - ( 3-1397 )
جلد 15 شماره 1 صفحات 28-3 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Shayegh F, Ghasemi F, Amirfatahi R, Sadri S, Ansarifard K. Online Single-Channel Seizure Prediction, Based on Seizure Genesis Model of Depth-EEG Signals Using Extended Kalman Filter. JSDP 2018; 15 (1) :3-28
URL: http://jsdp.rcisp.ac.ir/article-1-509-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-509-fa.html
شایق فرزانه، قاسمی فهیمه، امیر فتاحی رسول، صدری سعید، انصاری اصل کریم. پیش‌گویی برخط و تک‌کاناله وقوع حملههای صرعی با ارائه الگوی تولید صرع بر روی سیگنالهای depth-EEG با استفاده از فیلتر کالمن توسعهیافته. پردازش علائم و داده‌ها. 1397; 15 (1) :3-28
دانشگاه علوم پزشکی اصفهان
چکیده: (4966 مشاهده)
تاکنون برای پیش‌گویی وقوع حملههای صرعی تلاش‌های فراوانی انجام‌شده‌است و مؤثرترین این روش‌ها نیز بر پایه چگونگی ایجاد حمله صرعی، هم‌زمانی بین کانال‌های متفاوت، ثبت فعالیت مغز را موردتوجه قرار دادهاند. این روش‌ها، برای رسیدن به دقت پیش‌گویی بالا، به تعداد زیادی از کانال‌های ثبت فعالیت مغز نیاز دارند و به همین دلیل در عمل مورداستفاده بیماران نخواهند‌بود. با توجه به این نکته که عامل ایجاد هم‌زمانی بین بخش‌های متفاوت مغز، میزان فعالیت مهاری و تحریکی در نورونهاست؛ انتظار میرود دقت پیش‌گویی وقوع حمله صرعی با توجه به میزان مهار و تحریک در نورونهای مغز بهبود یابد. در این مقاله برای شبیهسازی تولید خودبهخودیِ حمله صرعی، یک الگو فیزیولوژیک با یک الگو آماری فضای حالت (SSM) ترکیب شده‌است. شاخصه‌های الگوی فیزیولوژیک، میزان فعالیت مهاری و تحریکی نورونها و خروجی آن، سیگنالهای depth-EEG است. در این الگو فیزیولوژیک، تغییر میزان مهار و تحریک، به بروز رفتارهای متفاوتی در سیگنال فعالیت مغز در خروجی الگو منجر میشود. الگوی SSM برای شبیهسازی رفتار شاخصه‌های مهار و تحریک در الگو فیزیولوژیک استفاده شده است. با توجه به این الگو و با استفاده از یک فیلتر کالمن توسعهیافته، میتوان شاخصه‌های مهار و تحریک پنهان در سیگنالهای مغزی نوفه‌ای را به‌صورت برخط استخراج کرد. با در دست داشتن دنباله شاخصه‌های مهار و تحریک (به‌جای سیگنالهای depth-EEG)، رفتار شاخصه‌ها با استفاده از یک طبقه‌بندی‌کننده الگوی مارکوف مخفی پیوسته (CHMM) به دو گروه پیشازحمله و میانحملهای دسته‌بندی‌شده است. در انتها با روش پیشنهادی، دنباله شاخصه‌های مهار و تحریک سیگنال ثبتشده از یک کانال واقع در کانون صرع شش بیمار از پایگاه داده FSPEEG (که برای آن‌ها ثبت depth-EEG وجود دارد) استخراج‌شده است. کانون صرع این شش بیمار در هیپوکامپ و در بخش تمپورال قرار دارد. این سیگنالها شامل 24 حمله و حدود 144 ساعت سیگنال میانحملهای هستند. وقوع حمله صرعی در این بیماران در بدترین حالت ده دقیقه پیش از رخداد حمله صرعی پیشبینی شده است که برای انجام اقدامات درمانی مناسب است. میزان حساسیت و نرخ پیش‌گویی نادرست الگوریتم پیش‌گویی به‌طور میانگین به‌ترتیب برابر با 100% و 2/0 در ساعت است. در مقایسه با روش‌های پر‌محاسبه‌ای که برای رسیدن به دقت بالا به کانال‌های فراوانی نیاز دارند، پیش‌گویی مبتنی بر الگو با استفاده از یک کانال و به‌صورت کاملاً برخط از ویژگیهای این روش است.
واژه‌های کلیدی: الگو فیزیولوژیک حمله صرع، پیش‌گویی برخط وقوع حمله صرعی، فیلتر کالمن، الگو آماری فضای حالت (SSM)، صرع، الگوی مارکوف مخفی پیوسته (CHMM)
نوع مطالعه: پژوهشي |
موضوع مقاله:
مقالات گروه علائم حیاتی ( مرتبط با مهندسی پزشکی)
دریافت: 1396/12/11 | پذیرش: 1396/8/3 | انتشار: 1397/3/23 | انتشار الکترونیک: 1397/3/23
دریافت: 1396/12/11 | پذیرش: 1396/8/3 | انتشار: 1397/3/23 | انتشار الکترونیک: 1397/3/23
فهرست منابع
1. [1] F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, "Seizure prediction: the long and winding road," Brain, vol. 130, pp. 314-33, 2007. [DOI:10.1093/brain/awl241] [PMID]
2. [2] L. D. Iasemidis, J. Chris Sackellares, H. P. Zaveri, and W. J. Williams, "Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures," Brain Topography, vol. 2, pp. 187-201, 1990. [DOI:10.1007/BF01140588] [PMID]
3. [3] H. Kantz, "A robust method to estimate the maximal Lyapunov exponent of a time series," Physics Letters A, vol. 185, pp. 77-87, 1994. [DOI:10.1016/0375-9601(94)90991-1]
4. [4] M. T. Rosenstein, J. J. Collins, and C. J. De Luca, "A practical method for calculating largest Lyapunov exponents from small data sets," Physica D: Nonlinear Phenomena, vol. 65, pp. 117-134, 1993. [DOI:10.1016/0167-2789(93)90009-P]
5. [5] R. Q. Quiroga, J. Arnhold, K. Lehnertz, and P. Grassberger, "Kulback-Leibler and renormalized entropies: Applications to electroencephalograms of epilepsy patients," Physical Review E, vol. 62, pp. 8380-8386, 2000. [DOI:10.1103/PhysRevE.62.8380] [PMID]
6. [6] 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," Phys Rev E Stat Nonlin Soft Matter Phys, vol. 64, p. 061907, 2001. [DOI:10.1103/PhysRevE.64.061907] [PMID]
7. [7] A. S. Weigend, Time Series Prediction: Forecast-ing The Future And Understanding The Past Addison-Wesley, 1993.
8. [8] L. D. Iasemidis, P. Pardalos, J. C. Sackellares, and D. S. Shiau, "Quadratic Binary Programming and Dynamical System Approach to Determine the Predictability of Epileptic Seizures," Journal of Combinatorial Optimization, vol. 5, pp. 9-26, 2001. [DOI:10.1023/A:1009877331765]
9. [9] K. V. Mardia, Probability and Mathematical Stat-istics: Statistics of Directional Data. London: Academy, 1972.
10. [10] F. Mormann, K. Lehnertz, P. David, and C. E. Elger, "Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients," Physica D: Nonlinear Phenomena, vol. 144, pp. 358-369, 2000. [DOI:10.1016/S0167-2789(00)00087-7]
11. [11] F. Mormann, R. G. Andrzejak, T. Kreuz, C. Rieke, P. David, C. E. Elger, et al., "Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroence-phalogram recordings from epilepsy patients," Phys Rev E Stat Nonlin Soft Matter Phys, vol. 67, p. 021912, 2003. [DOI:10.1103/PhysRevE.67.021912] [PMID]
12. [12] J. Arnhold, P. Grassberger, K. Lehnertz, and C. E. Elger, "A robust method for detecting interdependences: application to intracranially recorded EEG," Physica D: Nonlinear Phenom-ena, vol. 134, pp. 419-430, 1999. [DOI:10.1016/S0167-2789(99)00140-2]
13. [13] K. Schindler, H. Leung, C. E. Elger, and K. Lehnertz, "Assessing seizure dynamics by analysing the correlation structure of multi-channel intracranial EEG," Brain, vol. 130, pp. 65-77, 2007. [DOI:10.1093/brain/awl304] [PMID]
14. [14] M. Winterhalder, B. Schelter, T. Maiwald, A. Brandt, A. Schad, A. Schulze-Bonhage, et al., "Spatio-temporal patient-individual assessment of synchronization changes for epileptic seizure prediction," Clin Neurophysiol, vol. 117, pp. 2399-413, 2006. [DOI:10.1016/j.clinph.2006.07.312] [PMID]
15. [15] R. G. Andrzejak, F. Mormann, T. Kreuz, C. Rieke, A. Kraskov, C. E. Elger, et al., "Testing the null hypothesis of the nonexistence of a preseizure state," Phys Rev E Stat Nonlin Soft Matter Phys, vol. 67, p. 010901, 2003. [DOI:10.1103/PhysRevE.67.010901] [PMID]
16. [16] M. A. Harrison, M. G. Frei, and I. Osorio, "Accumulated energy revisited," Clin Neurophy-siol, vol. 116, pp. 527-31, 2005. [DOI:10.1016/j.clinph.2004.08.022] [PMID]
17. [17] Y. C. Lai, M. A. Harrison, M. G. Frei, and I. Osorio, "Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures," Chaos, vol. 14, pp. 630-42, 2004. [DOI:10.1063/1.1777831] [PMID]
18. [18] Y.-C. Lai, M. A. F. Harrison, M. G. Frei, and I. Osorio, "Inability of Lyapunov Exponents to Predict Epileptic Seizures," Physical Review Letters, vol. 91, p. 068102, 2003. [DOI:10.1103/PhysRevLett.91.068102] [PMID]
19. [19] K. K. Jerger, S. L. Weinstein, T. Sauer, and S. J. Schiff, "Multivariate linear discrimination of seizures," Clin Neurophysiol, vol. 116, pp. 545-51, 2005. [DOI:10.1016/j.clinph.2004.08.023] [PMID]
20. [20] A. A. Bruzzo, B. Gesierich, M. Santi, C. A. Tassinari, N. Birbaumer, and G. Rubboli, "Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. A preliminary study," Neurol Sci, vol. 29, pp. 3-9, 2008. [DOI:10.1007/s10072-008-0851-3] [PMID]
21. [21] X. Li, G. Ouyang, and D. A. Richards, "Predictability analysis of absence seizures with permutation entropy," Epilepsy Res, vol. 77, pp. 70-4, 2007. [DOI:10.1016/j.eplepsyres.2007.08.002] [PMID]
22. [22] A. S. Zandi, G. A. Dumont, M. Javidan, and R. Tafreshi, "An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG," Conf Proc IEEE Eng Med Biol Soc, vol. 2009, pp. 228-31, 2009. [DOI:10.1109/IEMBS.2009.5333971]
23. [23] X. Huang, Z. Song, and W. Zhen, "Effect of power spectral entropy on the prediction of seizure in epileptic rats," Journal of Central South University. Medical Sciences, vol. 34, pp. 776-80, 2009. [PMID]
24. [24] Z. Vahabi, R. Amirfattahi, F. Shayegh, F, Ghassemi, "Online epileptic seizure prediction using wavelet-based bi-phase correlation of electrical signals tomography." International journal of neural systems, vol. 25, no. 6, 1550028, 2015. [DOI:10.1142/S0129065715500288] [PMID]
25. [25] C. Komalapriya, M. C. Romano, M. Thiel, U. Schwarz, J. Kurths, J. Simonotto, et al., "Analysis of high-resolution microelectrode EEG recording in an animal model of spontaneous limbic seizure," International Journal of Bifurcation and Chaos, vol. 19, pp. 605-617, 2009. [DOI:10.1142/S0218127409023226]
26. [26] T. Schluter and S. Conrad, "Hidden markov model-based time series prediction using motifs for detecting inter-time-serial correlations," presented at the Proceedings of the 27th Annual ACM Symposium on Applied Computing, Trento, Italy, 2012. [DOI:10.1145/2245276.2245308]
27. [27] I. Osorio, M. G. Frei, D. Sornette, and J. Milton, "Pharmaco-resistant seizures: self-triggering capacity, scale-free properties and predicta-bility?," Eur J Neurosci, vol. 30, pp. 1554-8, 2009. [DOI:10.1111/j.1460-9568.2009.06923.x] [PMID]
28. [28] A. Shahidi Zandi, R. Tafreshi, M. Javidan, and G. A. Dumont, "Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG," Conf Proc IEEE Eng Med Biol Soc, vol. 2010, pp. 5537-40, 2010. [DOI:10.1109/IEMBS.2010.5626764]
29. [29] F. Kaffashi, "Variability Analysis & Its Applications To Physiological Time Series Data," Doctor of Philosophy, Department of Electrical Engineering & Computer Science, Case Western Reserve University, 2007.
30. [30] D. Kugiumtzis, I. Vlachos, A. Papana, and P. G. Larsson, "Assessment of Measures of Scalar Time SeriesAnalysis in Discriminating Preictal States," International Journal of Bioelectro-magnetism, vol. Vol. 9, pp. 134 - 145, 2007.
31. [31] M. D'Alessandro, R. Esteller, G. Vachtsevanos, A. Hinson, J. Echauz, and B. Litt, "Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients," IEEE Trans Biomed Eng, vol. 50, pp. 603-15, 2003.
https://doi.org/10.1109/TBME.2003.810706 [DOI:10.1109/TBME.2003.815899] [PMID]
32. [32] A. F. Rabbi, A. Aarabi, and R. Fazel-Rezai, "Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG," Conf Proc IEEE Eng Med Biol Soc, vol. 2010, pp. 3301-4, 2010. [DOI:10.1109/IEMBS.2010.5627247]
33. [33] A. J. Cadotte, T. B. DeMarse, T. H. Mareci, M. B. Parekh, S. S. Talathi, D.-U. Hwang, et al., "Granger causality relationships between local field potentials in an animal model of temporal lobe epilepsy," Journal of Neuroscience Methods, vol. 189, pp. 121-129, 2010. [DOI:10.1016/j.jneumeth.2010.03.007] [PMID] [PMCID]
34. [34] P. W. Mirowski, Y. Lecun, D. Madhavan, and R. Kuzniecky, "Comparing SVM and Convolutional Networks for Epileptic Seizure Prediction from Intracranial EEG," In 2008 IEEE Workshop on Machine Learning for Signal Processing, pp. 244-249. IEEE, 2008. [DOI:10.1109/MLSP.2008.4685487]
35. [35] R. Costa, P. Oliveira, G. Rodrigues, B. Leitão, and A. Dourado, "Epileptic Seizure Classifica-tion Using Neural Networks with 14 Features," in Knowledge-Based Intelligent Information and Engineering Systems, 2008, pp. 281-288.
36. [36] H. Feldwisch-Drentrup, B. Schelter, M. Jachan, J. Nawrath, J. Timmer, and A. Schulze-Bonhage, "Joining the benefits: combining epileptic seizure prediction methods," Epilepsia, vol. 51, pp. 1598-606, 2010. [DOI:10.1111/j.1528-1167.2009.02497.x] [PMID]
37. [37] G. R. Minasyan, J. B. Chatten, M. J. Chatten, and R. N. Harner, "Patient-specific early seizure detection from scalp electroencephalogram," Journal of clinical neurophysiology : official publication of the American Electroencephalo-graphic Society, vol. 27, pp. 163-178, 2010.
38. [38] P. Mirowski, D. Madhavan, Y. Lecun, and R. Kuzniecky, "Classification of patterns of EEG synchronization for seizure prediction," Clin Neurophysiol, vol. 120, pp. 1927-40, 2009. [DOI:10.1016/j.clinph.2009.09.002] [PMID]
39. [39] Park, L. Luo, K. K. Parhi, and T. Netoff, "Seizure prediction with spectral power of EEG using cost-sensitive support vector machines," Epilepsia, vol. 52, pp. 1761-1770, 2011. [DOI:10.1111/j.1528-1167.2011.03138.x] [PMID]
40. [40] A. Ventura, J. Franco, J. Ramos, B. Direito, and A. Dourado, "Epileptic Seizure Prediction and the Dimensionality Reduction Problem," presen-ted at the Artificial Neural Networks ( ICANN 2009), 2009.
41. [41] J. R. Williamson, D. W. Bliss, and D. W. Browne, "Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG," in IEEE International Confer-ence on Acoustics, Speech and Signal Processing (ICASSP), 2011 pp. 665-668.
42. [42] B. Direito, J. Duarte, C. Teixeira, B. Schelter, M. L. V. Quyen, A. Schulze-Bonhage, et al., "Feature selection in high dimensional EEG features spaces for epileptic seizure prediction," presented at the 18th IFAC World Congress, Milano (Italy), 2011. [DOI:10.3182/20110828-6-IT-1002.03331]
43. [43] F. M. H Soleimani, "On the Relationship between Right-brain and Left-brain Dominance and Reading Comprehension Test Performance of Iranian EFL Learners," Brain, Broad Research in Artificial Intelligence and Neuroscience, vol. 3, pp. 44-52, 2012.
44. [44] M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, "Phase Synchronization of Chaotic Oscillators," Physical Review Letters, vol. 76, pp. 1804-1807, 1996. [DOI:10.1103/PhysRevLett.76.1804] [PMID]
45. [45] L. Kuhlmann, A. Burkitt, M. Cook, K. Fuller, D. Grayden, L. Seiderer, et al., "Seizure Detection Using Seizure Probability Estimation: Com-parison of Features Used to Detect Seizur-es," Annals of Biomedical Engineering, vol. 37, pp. 2129-2145, 2009. [DOI:10.1007/s10439-009-9755-5] [PMID]
46. [46] L. Kuhlmann, D. Freestone, A. Lai, A. N. Burkitt, K. Fuller, D. B. Grayden, et al., "Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons," Epilepsy Research, vol. 91, pp. 214-231, 2010. [DOI:10.1016/j.eplepsyres.2010.07.014] [PMID]
47. [47] C. C. Liu, P. M. Pardalos, W. A. Chaovalitwongse, D. S. Shiau, G. Ghacibeh, W. Suharitdamrong, et al., "Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains," J Comb Optim, vol. 15, pp. 276-286, 2008. [DOI:10.1007/s10878-007-9118-9] [PMID] [PMCID]
48. [48] S. Bialonski and K. Lehnertz, "Identifying phase synchronization clusters in spatially extended dynamical systems," Physical Review E, vol. 74, p. 051909, 2006. [DOI:10.1103/PhysRevE.74.051909] [PMID]
49. [49] N. Mammone, J. C. Principe, F. C. Morabito, D. S. Shiau, and J. C. Sackellares, "Visualization and modelling of STLmax topographic brain acti-vity maps," Journal of Neuroscience Methods, vol. 189, pp. 281-294, 2010. [DOI:10.1016/j.jneumeth.2010.03.027] [PMID] [PMCID]
50. [50] K. Lehnertz, F. Mormann, H. Osterhage, A. Muller, J. Prusseit, A. Chernihovskyi, et al., "State-of-the-art of seizure prediction," J Clin Neurophysiol, vol. 24, pp. 147-53, 2007. [DOI:10.1097/WNP.0b013e3180336f16] [PMID]
51. [51] A. Ossadtchi, R. E. Greenblatt, V. L. Towle, M. H. Kohrman, and K. Kamada, "Inferring spatiotemporal network patterns from intracranial EEG data," Clinical Neurophysi-ology, vol. 121, pp. 823-835, 2010. [DOI:10.1016/j.clinph.2009.12.036] [PMID] [PMCID]
52. [52] I. Osorio and Y. C. Lai, "A phase-synchronization and random-matrix based approach to multi-channel time-series analysis with applica-tion to epilepsy," Chaos, vol. 21, p. 033108, 2011. [DOI:10.1063/1.3615642] [PMID] [PMCID]
53. [53] D. Krug, H. Osterhage, C. E. Elger, and K. Lehnertz, "Estimating nonlinear interdepend-ences in dynamical systems using cellular nonli-near networks," Physical Review E, vol. 76, p. 041916, 2007. [DOI:10.1103/PhysRevE.76.041916] [PMID]
54. [54] C. Rummel, F. Amor, H. Gast, and K. Schindler, "Applying multivariate symbolic interrelation measures to quantify peri-seizure EEG dynamics of focal onset seizures," Clinical neurophysio-logy : official journal of the Internat-ional Fed-eration of Clinical Neurophysiology, vol. 122, pp. e1-e2, 2011.
55. [55] D. Krug, C. Elger, and K. Lehnertz, "A CNN-based synchronization analysis for epileptic seizure prediction: Inter- and intraindividual generalization properties," presented at the 11th International Workshop on Cellular Neural Networks and their Applications, 2008. [DOI:10.1109/CNNA.2008.4588656]
56. [56] S. Wang, W. A. Chaovalitwongse, and S. Wong, "A novel reinforcement learning framework for online adaptive seizure prediction," BIBM, pp. 499-504, 2010. [DOI:10.1109/BIBM.2010.5706617]
57. [57] L. Chisci, A. Mavino, G. Perferi, M. Sciandrone, C. Anile, G. Colicchio, et al., "Real-time epileptic seizure prediction using AR models and support vector machines," IEEE Trans Biomed Eng, vol. 57, pp. 1124-32, 2010. [DOI:10.1109/TBME.2009.2038990] [PMID]
58. [58] T. Netoff, Y. Park, and K. Parhi, "Seizure prediction using cost-sensitive support vector machine," in Conf Proc IEEE Eng Med Biol Soc., 2009, pp. 3322-5. [DOI:10.1109/IEMBS.2009.5333711]
59. [59] C.-C. Liu, "Brain Dynamics, System Control And Optimization Techniques With Applications In Epilepsy," Doctor Of Philosophy, University Of Florida, 2008.
60. [60] G. Ouyang, X. Li, C. Dang, and D. A. Richards, "Deterministic dynamics of neural activity during absence seizures in rats," Physical Review E, vol. 79, p. 041146, 2009. [DOI:10.1103/PhysRevE.79.041146] [PMID]
61. [61] G. Ouyang, X. Li, C. Dang, and D. A. Richards, "Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats," Clinical Neurophysiology, vol. 119, pp. 1747-1755, 2008. [DOI:10.1016/j.clinph.2008.04.005] [PMID]
62. [62] G. van Luijtelaar, A. Hramov, E. Sitnikova, and A. Koronovskii, "Spike–wave discharges in WAG/Rij rats are preceded by delta and theta precursor activity in cortex and thalamus," Clinical Neurophysiology, vol. 122, pp. 687-695, 2011. [DOI:10.1016/j.clinph.2010.10.038] [PMID]
63. [63] D. Liu, Z. Pang, and Z. Wang, "Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network," EURASIP Journal on Advances in Signal Processing-Special issue on statistical signal processing in neuroscience, vol. 2009, pp. 1-10, 2009.
64. [64] K. Schindler, R. Wiest, M. Kollar, and F. Donati, "EEG analysis with simulated neuronal cell models helps to detect pre-seizure changes," Clinical Neurophysiology, vol. 113, pp. 604-614, 2002. [DOI:10.1016/S1388-2457(02)00032-9]
65. [65] S.-Y. Chong, H. Wagner, and A. Wulf, "Neural oscillators triggered by loading and hip orientation can generate activation patterns at the ankle during walking in humans," Medical and Biological Engineering and Computing, vol. 50, pp. 917-923, 2012. [DOI:10.1007/s11517-012-0944-2] [PMID]
66. [66] F. Wendling, F. Bartolomei, J. J. Bellanger, and P. Chauvel, "Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition," European Journal of Neuroscience, vol. 15, pp. 1499-1508, 2002. [DOI:10.1046/j.1460-9568.2002.01985.x] [PMID]
67. [67] F. Shayegh, J.-J. Bellanger, S. Sadri, R. Amirfattahi, K. Ansari-Asl, and L. Senhadji. "Analysis of the behavior of a seizure neural mass model using describing functions." Journal of medical signals and sensors, vol. 3, no. 1, p. 2, 2013. [PMID] [PMCID]
68. [68] F. Shayegh, S. Sadri, R. Amirfattahi, and K. Ansari-Asl, "Proposing a two-level stochastic model for epileptic seizure genesis," Journal of computational neuroscience, vol. 36, no. 1, pp. 39-53, 2014. [DOI:10.1007/s10827-013-0457-5] [PMID]
69. [69] P. Rajdev, M. P. Ward, J. Rickus, R. Worth, and P. P. Irazoqui, "Real-time seizure prediction from local field potentials using an adaptive Wiener algorithm," Comput Biol Med, vol. 40, pp. 97-108, 2010. [DOI:10.1016/j.compbiomed.2009.11.006] [PMID]
70. [70] I. Osorio, M. G. Frei, J. Giftakis, T. Peters, J. Ingram, M. Turnbull, et al., "Performance Reassessment of a Real-time Seizure-detection Algorithm on Long ECoG Series," Epilepsia, vol. 43, pp. 1522-1535, 2002. [DOI:10.1046/j.1528-1157.2002.11102.x] [PMID]
71. [71] I. Osorio, M. G. Frei, and S. B. Wilkinson, "Real-Time Automated Detection and Quantitative Analysis of Seizures and Short-Term Prediction of Clinical Onset," Epilepsia, vol. 39, pp. 615-627, 1998. [DOI:10.1111/j.1528-1157.1998.tb01430.x] [PMID]
72. [72] B. Schelter, J. Timmer, and A. Schulze-Bonhage, Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications: John Wil-ey & Sons, 2008.
73. [73] P. V. Overschee and B. L. R. d. Moor, Subspace identification for linear systems: theory, implementation, applications vol. 1: Kluwer Academic Publishers, 1996. [DOI:10.1007/978-1-4613-0465-4]
74. [74] P. Zarchan and H. Musoff, Fundamentals Of Kalman Filtering: A Practical Approach vol. 208: AIAA, 2005.
75. [75] L. Baum, "An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes," Inequalities, vol. 3, pp. 1-8, 1972.
76. [76] L. Baum, T. Petrie, G. Soules, and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains," The Annals of Mathematical Statistics, vol. 41, pp. 164-171, 1970. [DOI:10.1214/aoms/1177697196]
77. [77] T. A. Lang, and M. Secic. How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. ACP Press, 2006.
78. [78] A. Schad, Ariane, K. Schindler, B. Schelter, T. Maiwald, A. Brandt, J. Timmer, and A. Schulze-Bonhage. "Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings." Clinical neurophysiology, no. 1, pp. 197-211, 2008. [DOI:10.1016/j.clinph.2007.09.130] [PMID]
79. [79] A. Aarabi, and Bin He. "Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach." Clinical Neurophysiolo-gy, vol. 125, no. 5, pp. 930-940, 2014. [DOI:10.1016/j.clinph.2013.10.051] [PMID] [PMCID]
80. [80]مهندس محمدرضا اسماعیلی*، دکتر سید حمید ظهیری، "تشخیص صرع در سیگنال EEG با استفاده از الگوریتم ابتکاری صفحات شیبدار(IPO)"، فصل‌نامه علمی پژوهشی پردازش علائم و داده‌ها، شماره 4، پیاپی 30، 1395
81. [80] Esmaeili M R, Zahiri S H. Epileptic seizure detection using Inclined Planes system Optimization algorithm(IPO). JSDP, 13 (4) :29-42, 2017. [DOI:10.18869/acadpub.jsdp.13.4.29]
ارسال پیام به نویسنده مسئول
| بازنشر اطلاعات | |
 |
این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
