Automated detection of the preseizure state in EEG signal using neural networks

• Autorzy: Sudalaimani C. , Sivakumaran N. , Elizabeth Thomas T. , Rominus Valsalam S.

Identyfikatory

DOI

10.1016/j.bbe.2018.11.007

• Języki publikacji: EN

Abstrakty

EN

The life-threatening neural syndrome epilepsy is elicited by seizure which affects over 50 million people in the universal. A seizure is a brain condition made by excessive, unusual exoneration by nerve cells of the brain. Contemporary seizure forecast research works exhibited worthy results in both undersized and lengthy electroencephalography (EEG) signal; however it is essential to formulate superior epileptic seizure forecast system; that shall be steady, constant and less resource intensive for effectively employed to heading for evolving a convenient and easily manageable ictal or seizure forewarning prearrangement or devices. Based on our exploration, we have found a novel seizure prediction method which we evaluated by producing ten sub-frequency EEG data from initially recorded signal. Simple, robust and computationally less-intense EEG characteristics are mined using the generated sub-frequency signals and applied the extracted features to computationally less intense generalized regression neural network (GRNN) to segregate EEG signal clips into normal or preseizure files. In this research work, we have engendered 10 sub-frequency bands of signals from original EEG recordings, extracted various meaningful features from those sub-frequency band signals, created 10 GRNN neural networks to categorize feature files as normal or preseizure, and then applied post-processing techniques with 10 thresholding mechanisms to each classifier output. As such, we determined that seizure fore-warning may function better in various sub-frequency bands for many patients in a subject-specific manner. We also found that epileptic-seizure forecast performed superior at '60 Hz high pass' filtered sub-frequency band EEG signal for all subjects or canines data.

Słowa kluczowe

EN

interictal; preictal; preseizure; seizure prediction; generalized regression neural network; subfrequency band

PL

przewidywanie napadu padaczkowego; sieć neuronowa; współrzędne regresji; pasmo częstotliwości

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 1
• Strony: 160--175
• Opis fizyczny: Bibliogr. 105 poz., rys., tab., wykr.

Twórcy

autor

Sudalaimani C.

• Instrumentation and Control Engineering (ICE), National Institute of Technology (NIT), Tiruchirappalli 620015, Tamil Nadu, India; Health and Software Technology Group (HSTG), Centre for Development of Advanced Computing (C-DAC), Thiruvananthapuram, Kerala, India

autor

Sivakumaran N.

• Instrumentation and Control Engineering (ICE), National Institute of Technology (NIT), Tiruchirappalli, Tamil Nadu, India

autor

Elizabeth Thomas T.

• Health and Software Technology Group (HSTG), Centre for Development of Advanced Computing (C-DAC), Thiruvananthapuram, Kerala, India

autor

Rominus Valsalam S.

• Health and Software Technology Group (HSTG), Centre for Development of Advanced Computing (C-DAC), Thiruvananthapuram, Kerala, India

Bibliografia

• [1] Parvez MZ, Paul M. EEG signal classification using frequency band analysis towards epileptic seizure prediction. 16th IEEE International Conference on Computer and Information Technology (ICCIT), Khulna, March. 2014. pp. 126–30.
• [2] https://www.kaggle.com/c/seizure-prediction.
• [3] Ali Yadollahpour, Mostafa Jalilifar. Seizure prediction methods: a review of the current predicting techniques. Biomed Pharmacol J 2014;7(1):153–62.
• [4] Nilufer Ozdemir, Esen Yildirim. Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers. Comput Math Methods Med 2014.
• [5] D'Alessandro Maryann, Esteller Rosana, Vachtsevanos George, Hinson Arthur, Echauz Javier, Litt Brian. Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients. IEEE Trans Biomed Eng 2003;50(May (5)).
• [6] Carney Paul R, Stephen Myers, Geyer James D. Seizure prediction: methods. Epilepsy Behav 2011;(December):22–8.
• [7] Schelter Bjorn, Winterhalder Matthias, Maiwald Thomas, et al. Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies. Epilepsia 2006;47 (12):2058–70.
• [8] Alotaiby TN, Alshebeili SA, Alshawi T, Ahmad I, Abd El- Samie FE. EEG seizure detection and prediction algorithms: a survey. EURASIP J Adv Signal Process 2014;2014:183, http://asp.eurasipjournals.com/content/2014/1/183.
• [9] Brinkmann Benjamin H, Wagenaar Joost, Abbot Drew, Adkins Phillip, Bosshard Simone C, Chen Min, et al. Crowd sourcing reproducible seizure forecasting in human and canine epilepsy. Brain 2016;139:1713–22. http://dx.doi.org/10.1093/brain/aww045.
• [10] Howbert JJ, Patterson EE, Stead SM, Brinkmann B, Vasoli V, Crepeau D, et al. Forecasting seizures in dogs with naturally occurring epilepsy. PLoS One 2014;9(1):e81920. http://dx.doi.org/10.1371/journal.pone.0081920.
• [11] Park Yun, Luo Lan, Parhi Keshab K, Theoden Netoff. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 2011;52 (10):1761–70. http://dx.doi.org/10.1111/j.1528-1167.2011.03138.x.
• [12] Jacobs Julia, Zelmann Rina, Jirsch Jeffrey, Chander Rahul, Chatillon Francois Dubeau Claude-Edouard, Gotman Jean. High frequency oscillations (80–500 Hz) in the preictal period in patients with focal seizures. Epilepsia 2009;50 (7):1780–92. http://dx.doi.org/10.1111/j.1528-1167.2009.02067.x.
• [13] Feldwisch-Drentrup Hinnerk, Schelter Bjorn, Jachan Michael, Nawrath Jakob, Timmer Jens, Schulze-Bonhage Andreas. Joining the benefits: combining epileptic seizure prediction methods. Epilepsia 2010;51(8):1598–606. http://dx.doi.org/10.1111/j.1528-1167.2009.02497.x.
• [14] Kiral-Kornek Isabell, Roy Subhrajit, Nurse Ewan, Mashford Benjamin, Karoly Philippa, Carroll Thomas, et al. Epileptic seizure prediction using big data and deep learning: Toward a mobile system. EBioMedicine 2018;27:103–11. http://dx.doi.org/10.1016/j.ebiom.2017.11.032.
• [15] Stacey William C. Seizure prediction is possible – Now let's make it practical. Elsevier B.V; 2018. http://dx.doi.org/10.1016/j.ebiom.2018.01.006.
• [16] Ramgopal Sriram, Thome-Souza Sigride, Jackson Michele, Ester Kadish Navah, Sánchez Fernández Iván, Klehm Jacquelyn, et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Elsevier Inc.; 2014. http://dx.doi.org/10.1016/j.yebeh.2014.06.023.
• [17] Nagaraj Vivek, Lee Steven, Krook-Magnuson Esther, Soltesz Ivan, Benquet Pascal, Irazoqui Pedro, et al. The future of seizure prediction and intervention: closing the loop. J Clin Neurophysiol 2015 June;32(3):194–206. http://dx.doi.org/10.1097/WNP.0000000000000139.
• [18] Varatharajah Yogatheesan, Iyer Ravishankar K, Berry Brent M, Worrell Gregory A, Brinkmann Benjamin H. Seizure forecasting and the preictal state in canine epilepsy. Int J Neural Syst 2017;27(February (1)):1650046. http://dx.doi.org/10.1142/S0129065716500465.
• [19] Alotaiby Turky N, Alshebeili Saleh A, Alotaibi Faisal M, Alrshoud Saud R. Epileptic seizure prediction using CSP and LDA for scalp EEG signals. Comput Intell Neurosci 2017;1–11.
• [20] Browne T, Holmes G. Handbook of epilepsy. Lippincott Williams and Wilkins; 2000.
• [21] Niedermeyer E, da Silva FL. Electroencephalography: basic principles, clinical applications and related fields. Lippincott Williams & Wilkins; 2004.
• [22] Blanco Susana, Garay Arturo, Coulombie Diego. Comparison of frequency bands using spectral entropy for epileptic seizure prediction. ISRN Neurol 2013.
• [23] Akiyama Tomoyuki, McCoy Blathnaid, Go Cristina Y, Ochi Ayako, et al. Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy. Epilepsia 2011;1–10.
• [24] Anusha KS, Mathew T, Mathews, Puthankattil Subha D. Classification of normal and epileptic EEG signal using time & frequency domain features through artificial neural network. International Conference on Advances in Computing and Communications; 2012.
• [25] Sun Mingui, Scheuer Mark L, Qian Shie, Baumann Stephen B, et al. Time-frequency analysis of high-frequency activity at the start of epileptic seizures. Proceedings – 19th International Conference – IEEE, October, Chicago, USA; 1997.
• [26] Franaszczuk PJ, Bergey GK. Time-frequency analysis of EEG signal complexity during epileptic seizures. Proceedings of the First Joint BMES/EMBS Conference, Serving Humanity, Advancing Technology, October 13–16, Atlanta, USA; 1999.
• [27] Zavid Parvez Mohammad, Paul Manoranjan. EEG signal classification using frequency band analysis towards epileptic seizure prediction. 16th Int'l Conf. Computer and Information Technology, March, Khulna, Bangladesh; 2014.
• [28] Mormann Florian, Andrzejak Ralph G, Elger Christian E, Lehnertz Klaus. Seizure prediction: the long and winding road. Brain March 2007;130(Pt 2):314–33. http://dx.doi.org/10.1093/brain/awl241.
• [29] Andrzejak Ralph G, Chicharro Daniel, Elger Christian E, Mormann Florian. Seizure prediction: any better than chance? Clin Neurophysiol 2009;120:1465–78. http://dx.doi.org/10.1016/j.clinph.2009.05.019.
• [30] Mormann Florian, Andrzejak Ralph G. Seizure prediction: making mileage on the long and winding road. Brain 2016;139(June (6)):1625–7. http://dx.doi.org/10.1093/brain/aww09.
• [31] Cook MJ, O'Brien TJ, Berkovic SF, Murphy M, Morokoff A, Fabinyi G, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol 2013;12(June (6)):563–71. http://dx.doi.org/10.1016/S1474-4422(13)70075-9.
• [32] Jeffry Howbert J, Edward E, Patterson S, Stead Matt, Brinkmann Ben, Vasoli Vincent, et al. Forecasting seizures in dogs with naturally occurring epilepsy. PLoS One 2014;9 (1):e81920. http://dx.doi.org/10.1371/journal.pone.0081920.
• [33] Fujita S, Toyoda I, Thamattoor AK, Buckmaster PS. Preictal activity of subicular, CA1, and dentate gyrus principal neurons in the dorsal hippocampus before spontaneous seizures in a rat model of temporal lobe epilepsy. J Neurosci 2014 Dec 10;34(50):16671–87. http://dx.doi.org/10.1523/JNEUROSCI.0584-14.2014.
• [34] WolfgangLöscher. Critical review of current animal models of seizures and epilepsy used in the discovery and development of new antiepileptic drugs. J Seizure 2011;20 (June (5)):359–68. http://dx.doi.org/10.1016/j.seizure.2011.01.003.
• [35] Sood Meenakshi, Bhooshan Sunil V. Sciences design and development of prediction model to detect seizure activity utilizing higher order statistical features of EEG signals. Res J Pharm Biol Chem 2014;5(May (3)):11–29.
• [36] Nikias CL. Higher-order spectral analysis. Engineering in medicine and biology society. Proceedings of the 15th Annual International Conference of the IEEE; 1993.
• [37] Mirowski Piotr, Madhavan Deepak, LeCun Yann, et al. Classification of patterns of EEG synchronization for seizure prediction. Machine Learning for Signal Processing. American Epilepsy Society Annual Meeting and IEEE Workshop; 2008.
• [38] Howbert JJ, Patterson EE, Stead SM, Brinkmann B, Vasoli V, et al. Forecasting seizures in dogs with naturally occurring epilepsy. PLoS One 2014;9(1):e81920. http://dx.doi.org/10.1371/journal.pone.0081920.
• [39] Park Yun, Luo Lan, Parhi Keshab K, Netoff Theoden. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 2011;52(10):1761–70. http://dx.doi.org/10.1111/j.1528-1167.2011.03138.x.
• [40] Patterson Edward E. Canine epilepsy: an underutilized model. ILAR J 2014;55(January (1)):182–6. http://dx.doi.org/10.1093/ilar/ilu021.
• [41] Leppik Ilo E, Patterson Edward N, Coles Lisa D, Craft Elise M, Cloyd James C. Canine status epilepticus: a translational platform for human therapeutic trials. Epilepsia 2011;52(Suppl 8):31–4. http://dx.doi.org/10.1111/j.1528-1167.2011.03231.x.
• [42] Tatum WO. Handbook of EEG interpretation. Demos Medical Publishing; 2014.
• [43] Baumgartner C, Series W, Leutmezer F, et al. Preictal SPECT in temporal lobe epilepsy: regional cerebral blood flow is increased prior to electroencephalography-seizure onset. J Nucl Med 1998;39(6):978–82.
• [44] Zandi AS, Tafreshi R, Javidan M, Dumont GA. Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG. Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'10); September 2010. p. 5537–40.
• [45] Zandi AS, Tafreshi R, Javidan M, Dumont GA. Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals. IEEE Trans Biomed Eng 2013;60(5):1401–13. http://dx.doi.org/10.1109/TBME.2012.2237399.
• [46] Aarabi A, He B. A rule-based seizure prediction method for focal neocortical epilepsy. Clin Neurophysiol 2012;123 (6):1111–22. http://dx.doi.org/10.1016/j.clinph.2012.01.014.
• [47] Schelter B, Feldwisch-Drentrup H, Ihle M, Schulze-Bonhage A, Timmer J. Seizure prediction in epilepsy: From circadian concepts via probabilistic forecasting to statistical evaluation. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS'11); September 2011. p. 1624–7.
• [48] Wang S, Chaovalitwongse WA, Wong S. A novel reinforcement learning framework for online adaptive seizure prediction. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM'10); December 2010. p. 499–504.
• [49] Li S, Zhou W, Yuan Q, Liu Y. Seizure prediction using spike rate of intracranial EEG. IEEE Trans Neural Syst Rehabil Eng 2013;21(6):880–6. http://dx.doi.org/10.1109/TNSRE.2013.2282153.
• [50] Niknazar H, Nasrabadi AM. Epileptic seizure prediction using a new similarity index for chaotic signals. Int J Bifurc Chaos 2016;26(11). http://dx.doi.org/10.1142/S0218127416501868.1650186.
• [51] Miri SMR, Nasrabadi AM. A new seizure prediction method based on return map. Proceedings of the 18th Iranian Conference of Biomedical Engineering (ICBME'11); December 2011. p. 244–8.
• [52] Rogowski Z, Gath I, Bental E. On the prediction of epileptic seizures. Biol Cybern 1981;42(1):9–15. http://dx.doi.org/10.1007/BF00335153.
• [53] Salant Y, Gath I, Henriksen O. Prediction of epileptic seizures from two-channel EEG. Med Biol Eng Comput 1998;36(5):549–56. http://dx.doi.org/10.1007/BF02524422.
• [54] Zhu T, Huang L, Tian X. Epileptic seizure prediction by using empirical mode decomposition and complexity analysis of single-channel scalp electroencephalogram. Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics (BMEI'09); October 2009. p. 1–4.
• [55] Zheng Y, Wang G, Li K, Bao G, Wang J. Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition. Clin Neurophysiol 2014;125(6):1104–11. http://dx.doi.org/10.1016/j.clinph.2013.09.047.
• [56] Williamson JR, Bliss DW, Browne DW, Narayanan JT. Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy Behav 2012;25(2):230–8. http://dx.doi.org/10.1016/j.yebeh.2012.07.007.
• [57] Kuhlmann L, Freestone D, Lai A, et al. Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons. Epilepsy Res 2010;91(2–3):214–31. http://dx.doi.org/10.1016/j.eplepsyres.2010.07.014.
• [58] Sackellares JC, Shiau D-S, Principe JC, et al. Predictability analysis for an automated seizure prediction algorithm. J Clin Neurophysiol 2006;23(6):509–20. http://dx.doi.org/10.1097/00004691-200612000-00003.
• [59] Bedeeuzzaman M, Fathima T, Khan YU, Farooq O. Seizure prediction using statistical dispersion measures of intracranial EEG. Biomed Signal Process Control 2014;10 (1):338–41. http://dx.doi.org/10.1016/j.bspc.2012.12.001.
• [60] Iasemidis L, Shiau D, Pardalos P, Chaovalitwongse W, Narayanan K. Long-term prospective on-line real-time seizure prediction. Clin Neurophysiol 2005;116:532–44. http://dx.doi.org/10.1016/j.clinph.2004.10.013.
• [61] Chaovalitwongse W, Iasemidis LD, Pardalos PM, Carney PR, Shiau D-S, Sackellares JC. Performance of a seizure warning algorithm based on the dynamics of intracranial EEG. Epilepsy Res 2005;64(3):93–113. http://dx.doi.org/10.1016/j.eplepsyres.2005.03.009.
• [62] Pardalos PM, Wanpracha C, Iasemidis LD, et al. Seizure warning algorithm based on optimization and nonlinear dynamics. Math Program 2004;101:365–85.
• [63] Elger CE, Lehnertz K. Seizure prediction by non-linear time series analysis of brain electrical activity. Eur J Neurosci 1998;10(2):786–9. http://dx.doi.org/10.1046/j.1460-9568.1998.00009.
• [64] Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol 2005;116(3):569–87. http://dx.doi.org/10.1016/j.clinph.2004.08.025.
• [65] Chisci L, Mavino A, Perferi G, et al. Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans Biomed Eng 2010;57(5):1124–32. http://dx.doi.org/10.1109/TBME.2009.2038990.
• [66] Hung S-H, Chao C-F, Wang S-K, Lin B-S, Lin C-T. VLSI implementation for epileptic seizure prediction system based on wavelet and chaos theory. Proceedings of the IEEE TENCON; November 2010. p. 364–8.
• [67] Chiang C-Y, Chang N-F, Chen T-C, Chen H-H, Chen L-G. Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS'11); September 2011. p. 7564–9.
• [68] Gadhoumi K, Lina J, Gotman J. Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clin Neurophysiol 2013;124 (9):1745–54. http://dx.doi.org/10.1016/j.clinph.2013.04.006.
• [69] Wang S, Chaovalitwongse WA, Wong S. Online seizure prediction using an adaptive learning approach. IEEE Trans Knowl Data Eng 2013;25(12):2854–66. http://dx.doi.org/10.1109/TKDE.2013.151.
• [70] Costa RP, Oliveira P, Rodrigues G, Direito B, Dourado A. Epileptic seizure classification using neural networks with 14 features. Knowledge-Based Intelligent Information and Engineering Systems: 12th International Conference, KES, Zagreb, Croatia. Part II, September 3–5. 2008. pp. 281–8.
• [71] [34pN] Bandarabadi M, Teixeira CA, Rasekhi J, Dourado A. Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol 2015;126(2):237– 48. http://dx.doi.org/10.1016/j.clinph.2014.05.022.
• [72] Vahabi Z, Amirfattahi R, Shayegh F, Ghassemi F. Online epileptic seizure prediction using wavelet-based bi-phase correlation of electrical signals tomography. Int J Neural Syst 2015;25(6). http://dx.doi.org/10.1142/S0129065715500288.1550028.
• [73] Myers MH, Padmanabha A, Hossain G, De Jongh Curry AL, Blaha CD. Seizure prediction and detection via phase and amplitude lock values. Front Hum Neurosci 2016;10. http://dx.doi.org/10.3389/fnhum.2016.00008. Article 80.
• [74] Consul S, Morshed BI, Kozma R. Hardware efficient seizure prediction algorithm. Nanosensors, Biosensors, and Info-Tech Sensors and Systems, San Diego, Calif, USA; March 2013.
• [75] Park Y, Luo L, Parhi KK, Netoff T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 2011;52(10):1761–70. http://dx.doi.org/10.1111/j.1528-1167.2011.03138.x.
• [76] Moghim N, Corne DW. Predicting epileptic seizures in advance. PLoS One 2014;9(6). http://dx.doi.org/10.1371/journal.pone.0099334.e99334.
• [77] Mirowski P, Madhavan D, LeCun Y, Kuzniecky R. Classification of patterns of EEG synchronization for seizure prediction. Clin Neurophysiol 2009;120(11):1927–40. http://dx.doi.org/10.1016/j.clinph.2009.09.002.
• [78] Ghaderyan P, Abbasi A, Sedaaghi MH. An efficient seizure prediction method using KNN-based undersampling and linear frequency measures. J Neurosci Methods 2014;232:134–42. http://dx.doi.org/10.1016/j.jneumeth.2014.05.019.
• [79] Direito B, Teixeira CA, Sales F, Castelo-Branco M, Dourado A. A realistic seizure prediction study based on multiclass SVM. Int J Neural Syst 2017;27(3). http://dx.doi.org/10.1142/S012906571750006X.1750006.
• [80] Bradley Andrew P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 1997;30(7):1145–59. http://citeseerx.ist.psu.edu/viewdoc/download?.doi=10.1. 1.93.3818&rep=rep1&type=pdf. (Accessed online at 2.03.2018, 4.58 pm).
• [81] Ramgopal Sriram. et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav 2014;37:291–307.
• [82] Brinkmann Benjamin H, et al. Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy. PLoS One 2015. 10.8.
• [83] Wong Kin Foon Kevin. et al. Modelling non-stationary variance in EEG time series by state space GARCH model. Comput Biol Med 2006;36.12:1327–35.
• [84] Schachter Steven C. Advances in the assessment of refractory epilepsy. Epilepsia 1993;34. s5.S24–30.
• [85] Park Yun. et al. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 2011;52(10). 1761-177.
• [86] Berendt M, Dam M. Epilepsy. Clinical neurology in small animals – localization, diagnosis and treatment; 2018. http://www.ivis.org/special_books/braund/berendt/IVIS. pdf?.q=epilepsy. (Accessed online at 25.3.10.36 am).
• [87] Kshirsagar Pravin R, Akojwar Sudhir G. Prediction of neurological disorders using optimized neural network. International Conference on Signal Processing, Communication, Power and Embedded System; 2016.
• [88] Hagan Martin T, Demuth Howard B, Beale Mark. Neural network design. Cengage learning; 2008.
• [89] Shivanandan SN, Sumathi S, Deepa SN. Introduction to neural networks using Matlab 6.0. Tata McGraw Hill Education Private Ltd.; 2010.
• [90] Alotaiby Turkey N, Alshebeili Saleh A, Alshawi Tariq, Ahmad Ishtiaq, Abd El-Samie Fathi E. EEG seizure detection and prediction algorithms: a survey. EURASIP J Adv Signal Process 2014;2014:183. http://asp.eurasipjournals.com/content/2014/1/183.
• [91] Donald F Specht. A general regression neural network. IEEE Trans Neural Netw 1991;2(November (6)).
• [92] Serap AYDIN. Determination of autoregressive model orders for seizure detection. Turk J Electr Eng Comput Sci 2010;18(1).
• [93] Aarabi A, Fazel-Rezai R, Aghakhani Y. EEG seizure prediction: measures and challenges. 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, September 2–6; 2009.
• [94] Dean Susan, Illowsky Barbara. Descriptive statistics: skewness and the mean, median, and mode; 2018, Connexions website. (Accessed on 28.02.6.54 am).
• [95] Jones DN, Gill CA. Comparing measures of sample skewness and kurtosis. J Roy Stat Soc Ser D Stat 1998;47 (1):183–9. http://dx.doi.org/10.1111/1467-9884.00122.
• [96] https://brownmath.com/stat/shape.htm. (Accessed on 29.03.2017, 9.00 pm).
• [97] Mouhammad Usman Syed, Usman Muhammad, Fong Simon. Epileptic seizure prediction using machine learning methods, Hindawi. Comput Math Methods Med 2017. http://dx.doi.org/10.1155/2017/9074759. Article ID 9074759, 10 pp.
• [98] Westfall PH. Kurtosis as peakedness, 1905 R.I.P.. Am Stat 2014;68:191–5. http://dx.doi.org/10.1080/00031305.2014.917055.
• [99] https://electronics.stackexchange.com/questions/77675/ definition-of-power-signals-and-energy-signals. (Accessed on 31.03.2018, 9.43 am).
• [100] Direito Bruno, Dourado António, Vieira Marco, Sales Francisco. Combining energy and wavelet transform for epileptic seizure prediction in an advanced computational system. IEEE International Conference on BioMedical Engineering and Informatics. IEEE; 2008. http://dx.doi.org/10.1109/BMEI.2008.276.
• [101] Rasekhi Jalil, Reza Karami Mollaei Mohammad, Bandarabadi Mojtaba, Teixeira César A, Dourado António. Epileptic seizure prediction based on ratio and differential linear univariate features. J Med Signals Sens 2015;5 (January–March (1)):1–11.
• [102] Padmasair Y, Subba Rao K, Malini V, Raghavendra Rao C. Linear prediction modelling for the analysis of the epileptic EEG. IEEE International Conference on Advances in Computer Engineering. IEEE; 2010. http://dx.doi.org/10.1109/ACE.2010.20.
• [103] Alkan Ahmet, Koklukaya Etem, Subasi Abdulhamit. Automatic seizure detection in EEG using logistic regression and artificial neural network. J Neurosci Methods 2005;148:167–76. http://dx.doi.org/10.1016/j.jneumeth.2005.04.009.
• [104] Kim Sun-Hee, Faloutsos Christos, Yang Hyung-Jeong. Coercively adjusted auto regression model for forecasting in epilepsy EEG. Comput Math Methods Med 2013;2013:545613. http://dx.doi.org/10.1155/2013/545613.
• [105] Sudalaimani C, Sivakumaran N, Sasi PM, Elizabeth TT, Rominus VS, Sathish E. Seizure prediction using general regression neural network. 9th International Conference on Trends in Trends in Measurement and Automation: TIMA. IEEE; 2017.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).