{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:15:37Z","timestamp":1773868537866,"version":"3.50.1"},"reference-count":105,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s13755-020-00129-1","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T22:02:24Z","timestamp":1602540144000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":92,"title":["Automated epilepsy detection techniques from electroencephalogram signals: a review study"],"prefix":"10.1007","volume":"8","author":[{"given":"Supriya","family":"Supriya","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2491-0546","authenticated-orcid":false,"given":"Siuly","family":"Siuly","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"129_CR1","unstructured":"\"Provider of Specialist Epilepsy Services | Epilepsy Action Australia\", Provider of Specialist Epilepsy Services | Epilepsy Action Australia, 2018. [Online]. Available: https:\/\/www.epilepsy.org.au\/about-epilepsy\/facts-and-statistics\/. Accessed 21 Dec 2018."},{"key":"129_CR2","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2991\/ijcis.11.1.51","volume":"11","author":"Kabir E, Siuly, Cao J, Wang H","year":"2018","unstructured":"Kabir E, Siuly, Cao J, Wang H. A computer aided analysis scheme for detecting epileptic seizure from EEG data. Int J Comput Intell Syst. 2018;11:663.","journal-title":"Int J Comput Intell Syst"},{"key":"129_CR3","first-page":"9","volume":"2019","author":"Zarei R, He J, Siuly, Zhang Y","year":"2019","unstructured":"Zarei R, He J, Siuly, Zhang Y. Exploring Douglas Peucker algorithm in the detection of epileptic seizure from multiclass EEG signals. BioMed Res Int. 2019;2019:9.","journal-title":"BioMed Res Int"},{"key":"129_CR4","doi-asserted-by":"crossref","unstructured":"Siuly, Li Y, Zhang Y. EEG signal analysis and classification: techniques and applications.\u00a0Health information science, Springer Nature, US (ISBN 978-3-319-47653-7). 2016.","DOI":"10.1007\/978-3-319-47653-7"},{"key":"129_CR5","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/j.1535-7511.2007.00207.x","volume":"7","author":"S Wiebe","year":"2007","unstructured":"Wiebe S, Hesdorffer D. Epilepsy: being ill in more ways than one. Epilepsy Curr. 2007;7:145\u20138.","journal-title":"Epilepsy Curr"},{"key":"129_CR6","doi-asserted-by":"crossref","first-page":"576","DOI":"10.4065\/71.6.576","volume":"71","author":"W Hauser","year":"1996","unstructured":"Hauser W, Annegers J, Rocca W. Descriptive epidemiology of epilepsy: contributions of population-based studies from Rochester, Minnesota. Mayo Clin Proc. 1996;71:576\u201386.","journal-title":"Mayo Clin Proc"},{"key":"129_CR7","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.seizure.2016.10.002","volume":"44","author":"L Jones","year":"2017","unstructured":"Jones L, Thomas R. Sudden death in epilepsy: insights from the last 25 years. Seizure. 2017;44:232\u20136.","journal-title":"Seizure"},{"key":"129_CR8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1049\/iet-smt.2018.5358","volume":"13","author":"S Siuly","year":"2019","unstructured":"Siuly S, Alcin O, Bajaj V, Sengur A, Zhang Y. Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure. IET Sci Meas Technol. 2019;13:35\u201341.","journal-title":"IET Sci Meas Technol"},{"key":"129_CR9","doi-asserted-by":"crossref","unstructured":"Supriya S, Siuly S, Wang H, Zhang Y. Weighted complex network-based framework for epilepsy detection from EEG signals, modelling and analysis of active biopotential signals in healthcare, volume 1, Chapter 3, pages 3\u20131 to 3\u201322,\u00a0August 2020 (Online ISBN:\u00a0978-0-7503-3279-8 and\u00a0Print ISBN: 978-0-7503-3277-4); 2020.","DOI":"10.1088\/978-0-7503-3279-8ch3"},{"issue":"3","key":"129_CR10","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.cmpb.2010.11.014","volume":"104","author":"Siuly, Li Y, Wen P","year":"2011","unstructured":"Siuly, Li Y, Wen P. Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Programs Biomed. 2011;104(3):358\u201372.","journal-title":"Comput Methods Programs Biomed"},{"key":"129_CR11","doi-asserted-by":"crossref","unstructured":"Siuly, Li Y, Wen P. Analysis and classification of EEG signals using a hybrid clustering technique. In: Proceedings of the 2010 IEEE\/ICME International Conference on Complex Medical Engineering (CME2010); 2010. p. 34\u201339.","DOI":"10.1109\/ICCME.2010.5558875"},{"key":"129_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2018.2876529","author":"S Supriya","year":"2018","unstructured":"Supriya S, Siuly S, Wang H, Zhang Y. EEG sleep stages analysis and classification based on weighed complex network features. IEEE Trans Emerg Top Comput Intell. 2018. https:\/\/doi.org\/10.1109\/TETCI.2018.2876529.","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"129_CR13","doi-asserted-by":"publisher","DOI":"10.1049\/iet-smt.2018.5358","author":"S Siuly","year":"2018","unstructured":"Siuly S, Al\u00e7in OF, Bajaj V, \u015eeng\u00fcr A, Zhang Y. Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure. IET Sci Meas Technol. 2018. https:\/\/doi.org\/10.1049\/iet-smt.2018.5358.","journal-title":"IET Sci Meas Technol"},{"key":"129_CR14","first-page":"2015","volume":"1\u201312","author":"S Siuly","year":"2015","unstructured":"Siuly S, Kabir E, Wang H, Zhang Y. Exploring sampling in the detection of multicategory EEG signals. Comput Math Methods Med. 2015;1\u201312:2015.","journal-title":"Comput Math Methods Med"},{"key":"129_CR15","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0013-4694(76)90063-8","volume":"41","author":"J Gotman","year":"1976","unstructured":"Gotman J, Gloor P. Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. Electroencephalogr Clin Neurophysiol. 1976;41:513\u201329.","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"129_CR16","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/0013-4694(79)90004-X","volume":"46","author":"J Gotman","year":"1979","unstructured":"Gotman J, Ives J, Gloor P. Automatic recognition of inter-ictal epileptic activity in prolonged EEG recordings. Electroencephalogr Clin Neurophysiol. 1979;46:510\u201320.","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"129_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2806890","volume":"16","author":"J Ma","year":"2016","unstructured":"Ma J, Sun L, Wang H, Zhang Y, Aickelin U. Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans Internet Technol. 2016;16:1\u201320.","journal-title":"ACM Trans Internet Technol"},{"key":"129_CR18","doi-asserted-by":"crossref","unstructured":"Hu H, Li J, Wang H, Daggard G. Combined gene selection methods for microarray data analysis. Lecture notes in computer science knowledge-based intelligent information and engineering systems; 2006. p. 976\u2013983.","DOI":"10.1007\/11892960_117"},{"key":"129_CR19","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s11280-017-0456-y","volume":"21","author":"M Peng","year":"2017","unstructured":"Peng M, Zeng G, Sun Z, Huang J, Wang H, Tian G. Personalized app recommendation based on app permissions. World Wide Web. 2017;21:89\u2013104.","journal-title":"World Wide Web"},{"issue":"3","key":"129_CR20","first-page":"1","volume":"16","author":"J Yin","year":"2019","unstructured":"Yin J, Cao J, Siuly S, Wang H. An integrated spectral-temporal analysis based framework for MCI detection using resting-state EEG signals. Int J Autom Comput. 2019;16(3):1\u201314.","journal-title":"Int J Autom Comput"},{"key":"129_CR21","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1504\/IJKWI.2009.027925","volume":"1","author":"F Khalil","year":"2009","unstructured":"Khalil F, Li J, Wang H. An integrated model for next page access prediction. Int J Knowl Web Intell. 2009;1:48.","journal-title":"Int J Knowl Web Intell"},{"key":"129_CR22","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s11280-013-0218-4","volume":"17","author":"J Zhang","year":"2013","unstructured":"Zhang J, Tao X, Wang H. Outlier detection from large distributed databases. World Wide Web. 2013;17:539\u201368.","journal-title":"World Wide Web"},{"key":"129_CR23","unstructured":"Khalil F, Li J, Wang H. markov model with clustering for predicting web page accesses. In: Proceeding of the 13th Australasian World Wide Web Conference (AusWeb07); 2007. p. 63\u201374."},{"key":"129_CR24","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1007\/s11280-017-0449-x","volume":"20","author":"H Li","year":"2017","unstructured":"Li H, Wang Y, Wang H, Zhou B. Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web. 2017;20:1507\u201325.","journal-title":"World Wide Web"},{"key":"129_CR25","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s11280-016-0390-4","volume":"20","author":"J Huang","year":"2016","unstructured":"Huang J, Peng M, Wang H, Cao J, Gao W, Zhang X. A probabilistic method for emerging topic tracking in Microblog stream. World Wide Web. 2016;20:325\u201350.","journal-title":"World Wide Web"},{"issue":"6","key":"129_CR26","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1007\/s11633-019-1178-7","volume":"16","author":"Siuly, Bajaj V, Sengur A, Zhang Y","year":"2019","unstructured":"Siuly, Bajaj V, Sengur A, Zhang Y. An advanced analysis system for identifying alcoholic brain state through EEG signals. Int J Autom Comput. 2019;16(6):737\u201347.","journal-title":"Int J Autom Comput"},{"key":"129_CR27","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/0020-7101(94)90076-0","volume":"35","author":"N Pradhan","year":"1994","unstructured":"Pradhan N, Dutt D. Data compression by linear prediction for storage and transmission of EEG signals. Int J Biomed Comput. 1994;35:207\u201317.","journal-title":"Int J Biomed Comput"},{"key":"129_CR28","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TBME.2007.905490","volume":"55","author":"S Ghosh-Dastidar","year":"2008","unstructured":"Ghosh-Dastidar S, Adeli H, Dadmehr N. Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans Biomed Eng. 2008;55:512\u20138.","journal-title":"IEEE Trans Biomed Eng"},{"key":"129_CR29","doi-asserted-by":"crossref","unstructured":"Sheoran P, Saini J. Epileptic seizure detection using PCA on wavelet subbands. In: 2014 5th International Conference\u2014Confluence The Next Generation Information Technology Summit (Confluence). 2014.","DOI":"10.1109\/CONFLUENCE.2014.6949361"},{"key":"129_CR30","unstructured":"Scholz M. Principal component analysis. 2006. https:\/\/www.nlpca.org\/pca_principal_component_analysis.html."},{"key":"129_CR31","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","volume":"13","author":"A Hyv\u00e4rinen","year":"2000","unstructured":"Hyv\u00e4rinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw. 2000;13:411\u201330.","journal-title":"Neural Netw"},{"key":"129_CR32","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TAU.1967.1161901","volume":"15","author":"P Welch","year":"1967","unstructured":"Welch P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust. 1967;15:70\u20133.","journal-title":"IEEE Trans Audio Electroacoust"},{"issue":"2","key":"129_CR33","first-page":"119","volume":"8","author":"C Yucelbas","year":"2013","unstructured":"Yucelbas C, Ozsen S, Gunes S, Yosunkaya S. Effect of some power spectral density estimation methods on automatic sleep stage scoring using artificial neural networks. IADIS Int J Comput Sci Inform Syst. 2013;8(2):119\u201331.","journal-title":"IADIS Int J Comput Sci Inform Syst"},{"key":"129_CR34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0010-4825(03)00060-X","volume":"34","author":"ED \u00dcbeyl\u0131","year":"2004","unstructured":"\u00dcbeyl\u0131 ED, G\u00fcler I. Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and ARMA methods. Comput Biol Med. 2004;34:293\u2013306.","journal-title":"Comput Biol Med"},{"key":"129_CR35","unstructured":"https:\/\/www.cs.colostate.edu\/eeg\/talks\/spr98\/6.html."},{"key":"129_CR36","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/5.488704","volume":"84","author":"M Unser","year":"1996","unstructured":"Unser M, Aldroubi A. A review of wavelets in biomedical applications. Proc IEEE. 1996;84:626\u201338.","journal-title":"Proc IEEE"},{"key":"129_CR37","unstructured":"Application Areas, https:\/\/www.wolfram.com\/mathematica\/new-in-8\/wavelet-analysis\/lifting-wavelet-transform-(lwt).html."},{"key":"129_CR38","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/1475-925X-10-38","volume":"10","author":"RJ Oweis","year":"2011","unstructured":"Oweis RJ, Abdulhay EW. Seizure classification in EEG signals utilizing Hilbert-Huang transform. BioMed Eng OnLine. 2011;10:38.","journal-title":"BioMed Eng OnLine"},{"key":"129_CR39","first-page":"1","volume":"2","author":"A Lee","year":"2015","unstructured":"Lee A, Altenm\u00fcller E. Detecting position dependent tremor with the Empirical mode decomposition. J Clin Mov Disord. 2015;2:1\u20136.","journal-title":"J Clin Mov Disord"},{"key":"129_CR40","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1038\/s41598-017-02665-5","volume":"7","author":"W M\u00fcller","year":"2017","unstructured":"M\u00fcller W, Jung A, Ahammer H. Advantages and problems of nonlinear methods applied to analyze physiological time signals: human balance control as an example. Sci Rep. 2017;7:2464.","journal-title":"Sci Rep"},{"key":"129_CR41","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1142\/S0129065711002912","volume":"21","author":"UR Acharya","year":"2011","unstructured":"Acharya UR, Sree SV, Suri JS. Automatic detection of epileptic EEG signals using higher order cumulant features. Int J Neural Syst. 2011;21:403\u201314.","journal-title":"Int J Neural Syst"},{"key":"129_CR42","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1111\/jtsa.12160","volume":"37","author":"M Fragkeskou","year":"2015","unstructured":"Fragkeskou M, Paparoditis E. Inference for the fourth-order innovation cumulant in linear time series. J Time Ser Anal. 2015;37:240\u201366.","journal-title":"J Time Ser Anal"},{"key":"129_CR43","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1209\/0295-5075\/4\/9\/004","volume":"4","author":"J-P Eckmann","year":"1987","unstructured":"Eckmann J-P, Kamphorst SO, Ruelle D. Recurrence plots of dynamical systems. Europhys Lett (EPL). 1987;4:973\u20137.","journal-title":"Europhys Lett (EPL)"},{"key":"129_CR44","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1016\/j.clinph.2003.12.023","volume":"115","author":"AA Marino","year":"2004","unstructured":"Marino AA, Nilsen E, Chesson AL, Frilot C. Effect of low-frequency magnetic fields on brain electrical activity in human subjects. Clin Neurophysiol. 2004;115:1195\u2013201.","journal-title":"Clin Neurophysiol"},{"key":"129_CR45","doi-asserted-by":"crossref","first-page":"227","DOI":"10.32598\/bcn.9.4.227","volume":"9","author":"B Akbarian","year":"2018","unstructured":"Akbarian B, Erfanian A. Automatic seizure detection based on nonlinear dynamical analysis of EEG signals and mutual information. Basic Clin Neurosci J. 2018;9:227\u201340.","journal-title":"Basic Clin Neurosci J"},{"key":"129_CR46","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TPWRS.2015.2407894","volume":"31","author":"P Bhui","year":"2016","unstructured":"Bhui P, Senroy N. Application of recurrence quantification analysis to power system dynamic studies. IEEE Trans Power Syst. 2016;31:581\u201391.","journal-title":"IEEE Trans Power Syst"},{"key":"129_CR47","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1179\/1743132812Y.0000000010","volume":"34","author":"S Carrubba","year":"2012","unstructured":"Carrubba S, Minagar A, Chesson AL, Frilot C, Marino AA. Increased determinism in brain electrical activity occurs in association with multiple sclerosis. Neurol Res. 2012;34:286\u201390.","journal-title":"Neurol Res"},{"key":"129_CR48","unstructured":"Pincus S. Approximate entropy: a complexity measure for biological time series data. In: Proceedings of the 1991 IEEE Seventeenth Annual Northeast Bioengineering Conference. 1991."},{"key":"129_CR49","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","volume":"278","author":"JS Richman","year":"2000","unstructured":"Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278:H2039\u2013H20492049.","journal-title":"Am J Physiol Heart Circ Physiol"},{"key":"129_CR50","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.71.021906","volume":"71","author":"M Costa","year":"2005","unstructured":"Costa M, Goldberger AL, Peng C-K. Multiscale entropy analysis of biological signals. Phys Rev E. 2005;71:021906.","journal-title":"Phys Rev E"},{"key":"129_CR51","first-page":"103","volume-title":"Understanding complex systems applications of chaos and nonlinear dynamics in science and engineering","author":"R Uthayakumar","year":"2013","unstructured":"Uthayakumar R. Fractal dimension in epileptic EEG signal analysis. Understanding complex systems applications of chaos and nonlinear dynamics in science and engineering, vol. 3. Berlin: Springer; 2013. p. 103\u2013157."},{"key":"129_CR52","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1088\/1741-2560\/2\/2\/002","volume":"2","author":"X Li","year":"2005","unstructured":"Li X, Polygiannakis J, Kapiris P, Peratzakis A, Eftaxias K, Yao X. Fractal spectral analysis of pre-epileptic seizures in terms of criticality. J Neural Eng. 2005;2:11\u20136.","journal-title":"J Neural Eng"},{"key":"129_CR53","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1007\/978-0-387-21830-4_12","volume-title":"The theory of chaotic attractors","author":"P Grassberger","year":"2004","unstructured":"Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. The theory of chaotic attractors. New York: Springer; 2004. p. 170\u2013189."},{"key":"129_CR54","doi-asserted-by":"crossref","unstructured":"Caesarendra W, Kosasih B, Tieu K, Moodie CAS. An application of nonlinear feature extraction\u2014a case study for low speed slewing bearing condition monitoring and prognosis. In: 2013 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics. 2013.","DOI":"10.1109\/AIM.2013.6584344"},{"key":"129_CR55","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0165-0270(02)00340-0","volume":"123","author":"H Adeli","year":"2003","unstructured":"Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods. 2003;123:69\u201387.","journal-title":"J Neurosci Methods"},{"key":"129_CR56","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/0167-2789(93)90009-P","volume":"65","author":"MT Rosenstein","year":"1993","unstructured":"Rosenstein MT, Collins JJ, Luca CJD. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D. 1993;65:117\u201334.","journal-title":"Physica D"},{"key":"129_CR57","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1111\/j.1528-1157.1973.tb03975.x","volume":"14","author":"PF Prior","year":"1973","unstructured":"Prior PF, Virden RSM, Maynard DE. An EEG device for monitoring seizure discharges. Epilepsia. 1973;14:367\u201372.","journal-title":"Epilepsia"},{"key":"129_CR58","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/0013-4694(74)90036-4","volume":"37","author":"TL Babb","year":"1974","unstructured":"Babb TL, Mariani E, Crandall PH. An electronic circuit for detection of EEG seizures recorded with implanted electrodes. Electroencephalogr Clin Neurophysiol. 1974;37:305\u20138.","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"129_CR59","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/0013-4694(82)90038-4","volume":"54","author":"J Gotman","year":"1982","unstructured":"Gotman J. Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol. 1982;54:530\u201340.","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"129_CR60","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/0013-4694(90)90032-F","volume":"76","author":"J Gotman","year":"1990","unstructured":"Gotman J. Automatic seizure detection: improvements and evaluation. Electroencephalogr Clin Neurophysiol. 1990;76:317\u201324.","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"129_CR61","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/0013-4694(93)90079-B","volume":"86","author":"H Qu","year":"1993","unstructured":"Qu H, Gotman J. Improvement in seizure detection performance by automatic adaptation to the EEG of each patient. Electroencephalogr Clin Neurophysiol. 1993;86:79\u2013877.","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"129_CR62","unstructured":"Qu H. Self-adapting Algorithms for Seizure Detection during EEG Monitoring. PhD dissertation, McGill University, 1995."},{"key":"129_CR63","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1212\/WNL.45.12.2250","volume":"45","author":"H Qu","year":"1995","unstructured":"Qu H, Gotman J. A seizure warning system for long-term epilepsy monitoring. Neurology. 1995;45:2250\u20134.","journal-title":"Neurology"},{"key":"129_CR64","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/10.552241","volume":"44","author":"H Qu","year":"1997","unstructured":"Qu H, Gotman J. A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device. IEEE Trans Biomed Eng. 1997;44:115\u201322.","journal-title":"IEEE Trans Biomed Eng"},{"key":"129_CR65","doi-asserted-by":"crossref","unstructured":"Jahankhani P, Kodogiannis V, Revett K. EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA06). 2006.","DOI":"10.1109\/JVA.2006.17"},{"key":"129_CR66","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1016\/j.eswa.2007.12.065","volume":"36","author":"H Ocak","year":"2009","unstructured":"Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl. 2009;36:2027\u201336.","journal-title":"Expert Syst Appl"},{"key":"129_CR67","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.cmpb.2005.06.012","volume":"80","author":"N Kannathal","year":"2005","unstructured":"Kannathal N, Choo ML, Acharya UR, Sadasivan P. Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed. 2005;80:187\u201394.","journal-title":"Comput Methods Programs Biomed"},{"key":"129_CR68","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.amc.2006.09.022","volume":"187","author":"K Polat","year":"2007","unstructured":"Polat K, G\u00fcne\u015f S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput. 2007;187:1017\u201326.","journal-title":"Appl Math Comput"},{"key":"129_CR69","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1016\/j.eswa.2007.02.009","volume":"34","author":"K Polat","year":"2008","unstructured":"Polat K, G\u00fcne\u015f 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 Syst Appl. 2008;34:2039\u201348.","journal-title":"Expert Syst Appl"},{"key":"129_CR70","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s40708-015-0030-2","volume":"3","author":"Kabir E, Siuly, Zhang Y","year":"2016","unstructured":"Kabir E, Siuly, Zhang Y. Epileptic seizure detection from EEG signals using logistic model trees. Brain Inform. 2016;3:93\u2013100.","journal-title":"Brain Inform"},{"key":"129_CR71","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.cmpb.2015.01.002","volume":"119","author":"S Siuly","year":"2015","unstructured":"Siuly S, Li Y. Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification. Comput Methods Programs Biomed. 2015;119:29\u2013422.","journal-title":"Comput Methods Programs Biomed"},{"key":"129_CR72","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neucom.2016.08.050","volume":"218","author":"\u00d6F Al\u00e7in","year":"2016","unstructured":"Al\u00e7in \u00d6F, Siuly S, Bajaj V, Guo Y, \u015eengur A, Zhang Y. Multi-category EEG signal classification developing time-frequency texture features based Fisher Vector encoding method. Neurocomputing. 2016;218:251\u20138.","journal-title":"Neurocomputing"},{"key":"129_CR73","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.engappai.2014.05.011","volume":"34","author":"Siuly, Li Y","year":"2014","unstructured":"Siuly, Li Y. A novel statistical algorithm for multiclass EEG signal classification. Eng Appl Artif Intell. 2014;34:154\u201367.","journal-title":"Eng Appl Artif Intell"},{"key":"129_CR74","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.cmpb.2010.11.014","volume":"104","author":"Siuly, Li Y, Wen P","year":"2011","unstructured":"Siuly, Li Y, Wen P. Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Programs Biomed. 2011;104:358\u201372.","journal-title":"Comput Methods Programs Biomed"},{"key":"129_CR75","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/978-3-319-47653-7_6","volume-title":"Health information science EEG signal analysis and classification","author":"S Siuly","year":"2016","unstructured":"Siuly S, Li Y, Zhang Y. A statistical framework for classifying epileptic seizure from multi-category EEG signals. Health information science EEG signal analysis and classification. New York: Springer; 2016. p. 99\u2013125."},{"key":"129_CR76","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1007\/s10916-010-9433-z","volume":"35","author":"K Chua","year":"2010","unstructured":"Chua K, Chandran V, Acharya U, Lim C. Application of higher order spectra to identify epileptic EEG. J Med Syst. 2010;35:1563\u201371.","journal-title":"J Med Syst"},{"key":"129_CR77","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.1016\/j.eswa.2009.09.051","volume":"37","author":"SP Kumar","year":"2010","unstructured":"Kumar SP, Sriraam N, Benakop PG, Jinaga BC. Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl. 2010;37:3284\u201391.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"129_CR78","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.cmpb.2005.06.005","volume":"80","author":"N Kannathal","year":"2005","unstructured":"Kannathal N, Acharya UR, Lim CM, Weiming Q, Hidayat M, Sadasivan PK. Characterization of EEG: a comparative study. Comput Methods Programs Biomed. 2005;80(1):17\u201323.","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"129_CR79","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10916-005-6133-1","volume":"29","author":"V Srinivasan","year":"2005","unstructured":"Srinivasan V, Eswaran C, Sriraam N. Artificial neural network-based epileptic detection using time-domain and frequency-domain features. J Med Syst. 2005;29(6):647\u201360.","journal-title":"J Med Syst"},{"key":"129_CR80","doi-asserted-by":"crossref","unstructured":"Belhadj S, Attia A, Adnane AB, Ahmed-Foitih Z, Taleb AA. Whole-brain epileptic seizure detection using unsupervised classification. In: Modelling, Identification and Control (ICMIC), 2016 8th International Conference on (pp. 977\u2013982). IEEE. 2016.","DOI":"10.1109\/ICMIC.2016.7804256"},{"issue":"4","key":"129_CR81","first-page":"1105","volume":"61","author":"M Shoaib","year":"2014","unstructured":"Shoaib M, Lee KH, Jha NK, Verma N. A 0.6\u2013107 \u00b5W energy-scalable processor for directly analyzing compressively-sensed EEG. IEEE Trans Circ Syst I. 2014;61(4):1105\u201318.","journal-title":"IEEE Trans Circ Syst I"},{"issue":"5","key":"129_CR82","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s10916-008-9145-9","volume":"32","author":"K Aslan","year":"2008","unstructured":"Aslan K, Bozdemir H, \u015eahin C, O\u011fulata SN, Erol R. A radial basis function neural network model for classification of epilepsy using EEG signals. J Med Syst. 2008;32(5):403\u20138.","journal-title":"J Med Syst"},{"issue":"3","key":"129_CR83","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.eswa.2005.04.011","volume":"29","author":"NF Guler","year":"2005","unstructured":"Guler NF, Ubey ED, Guler I. Recurrent neural network employing Lyapunov exponents for EEG signals classification. Expert Syst Appl. 2005;29(3):506\u201314.","journal-title":"Expert Syst Appl"},{"key":"129_CR84","doi-asserted-by":"crossref","first-page":"138834","DOI":"10.1109\/ACCESS.2020.3011877","volume":"8","author":"S Sheykhivand","year":"2020","unstructured":"Sheykhivand S, Rezaii T, Mousavi Z, Delpak A, Farzamnia A. Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification. IEEE Access. 2020;8:138834\u2013455.","journal-title":"IEEE Access"},{"key":"129_CR85","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neulet.2018.10.062","volume":"694","author":"O Fasil","year":"2019","unstructured":"Fasil O, Rajesh R. Time-domain exponential energy for epileptic EEG signal classification. Neurosci Lett. 2019;694:1\u20138.","journal-title":"Neurosci Lett"},{"key":"129_CR86","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TIM.2018.2855518","volume":"68","author":"S Lahmiri","year":"2019","unstructured":"Lahmiri S, Shmuel A. Accurate classification of seizure and seizure-free intervals of intracranial EEG signals from epileptic patients. IEEE Trans Instrum Meas. 2019;68:791\u20136.","journal-title":"IEEE Trans Instrum Meas"},{"key":"129_CR87","doi-asserted-by":"crossref","first-page":"105333","DOI":"10.1016\/j.knosys.2019.105333","volume":"191","author":"A Hassan","year":"2020","unstructured":"Hassan A, Subasi A, Zhang Y. Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowl-Based Syst. 2020;191:105333.","journal-title":"Knowl-Based Syst"},{"key":"129_CR88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/5173589","volume":"2019","author":"R Zarei","year":"2019","unstructured":"Zarei R, He J, Siuly S, Huang G, Zhang Y. Exploring Douglas-Peucker algorithm in the detection of epileptic seizure from multicategory EEG signals. Biomed Res Int. 2019;2019:1\u201319.","journal-title":"Biomed Res Int"},{"key":"129_CR89","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.jneumeth.2018.11.014","volume":"312","author":"H Al Ghayab","year":"2019","unstructured":"Al Ghayab H, Li Y, Siuly S, Abdulla S. A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification. J Neurosci Methods. 2019;312:43\u201352.","journal-title":"J Neurosci Methods"},{"key":"129_CR90","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s00500-018-3487-0","volume":"23","author":"H Al Ghayab","year":"2018","unstructured":"Al Ghayab H, Li Y, Siuly S, Abdulla S. Epileptic seizures detection in EEGs blending frequency domain with information gain technique. Soft Comput. 2018;23:227\u201339.","journal-title":"Soft Comput"},{"key":"129_CR91","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1515\/bmt-2019-0001","volume":"65","author":"C Mahjoub","year":"2020","unstructured":"Mahjoub C, Le Bouquin Jeann\u00e8s R, Lajnef T, Kachouri A. Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods. Biomed Eng. 2020;65:33\u201350.","journal-title":"Biomed Eng"},{"key":"129_CR92","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3389\/fnhum.2019.00052","volume":"13","author":"X Wang","year":"2019","unstructured":"Wang X, Gong G, Li N, Qiu S. Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization. Front Hum Neurosci. 2019;13:52.","journal-title":"Front Hum Neurosci"},{"key":"129_CR93","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1007\/s40846-019-00467-w","volume":"39","author":"A Garc\u00e9s Correa","year":"2019","unstructured":"Garc\u00e9s Correa A, Orosco L, Diez P, Laciar Leber E. Adaptive filtering for epileptic event detection in the EEG. J Med Biol Eng. 2019;39:912\u20138.","journal-title":"J Med Biol Eng"},{"key":"129_CR94","doi-asserted-by":"crossref","first-page":"607","DOI":"10.3389\/fphys.2020.00607","volume":"11","author":"S Aung","year":"2020","unstructured":"Aung S, Wongsawat Y. Modified-distribution entropy as the features for the detection of epileptic seizures. Front Physiol. 2020;11:607.","journal-title":"Front Physiol"},{"key":"129_CR95","doi-asserted-by":"crossref","first-page":"61046","DOI":"10.1109\/ACCESS.2019.2915610","volume":"7","author":"S Chen","year":"2019","unstructured":"Chen S, Zhang X, Chen L, Yang Z. Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access. 2019;7:61046\u201356.","journal-title":"IEEE Access"},{"key":"129_CR96","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s10916-019-1234-4","volume":"43","author":"R Selvakumari","year":"2019","unstructured":"Selvakumari R, Mahalakshmi M, Prashalee P. Patient-specific seizure detection method using hybrid classifier with optimized electrodes. J Med Syst. 2019;43:121.","journal-title":"J Med Syst"},{"key":"129_CR97","doi-asserted-by":"crossref","first-page":"140","DOI":"10.3390\/e22020140","volume":"22","author":"J Wu","year":"2020","unstructured":"Wu J, Zhou T, Li T. Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient boosting. Entropy. 2020;22:140.","journal-title":"Entropy"},{"key":"129_CR98","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.3390\/sym12081239","volume":"12","author":"S Jang","year":"2020","unstructured":"Jang S, Lee S. Detection of epileptic seizures using wavelet transform, peak extraction and PSR from EEG signals. Symmetry. 2020;12:1239.","journal-title":"Symmetry"},{"key":"129_CR99","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1049\/el.2016.1992","volume":"52","author":"S Supriya","year":"2016","unstructured":"Supriya S, Siuly S, Zhang Y. Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network. Electron Lett. 2016;52:1430\u20132.","journal-title":"Electron Lett"},{"key":"129_CR100","doi-asserted-by":"crossref","first-page":"6554","DOI":"10.1109\/ACCESS.2016.2612242","volume":"4","author":"S Supriya","year":"2016","unstructured":"Supriya S, Siuly S, Wang H, Cao J, Zhang Y. Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access. 2016;4:6554\u2013666.","journal-title":"IEEE Access"},{"key":"129_CR101","doi-asserted-by":"crossref","unstructured":"Supriya, Siuly, Wang H, Zhuo G, Zhang Y. Analyzing EEG signal data for detection of epileptic seizure: introducing weight on visibility graph with complex network feature. Lecture notes in computer science databases theory and applications; 2016, p. 56\u201366","DOI":"10.1007\/978-3-319-46922-5_5"},{"key":"129_CR102","doi-asserted-by":"crossref","unstructured":"Supriya, Siuly, Wang H, Zhang Y. An efficient framework for the analysis of Big Brain Signals Data. Lecture notes in computer science databases theory and applications; 2018. p. 199\u2013207.","DOI":"10.1007\/978-3-319-92013-9_16"},{"key":"129_CR103","doi-asserted-by":"crossref","unstructured":"Zhu G, Li Y, Wen P. Analysing epileptic EEGs with a visibility graph algorithm. In: 2012 5th International Conference on BioMedical Engineering and Informatics; 2012.","DOI":"10.1109\/BMEI.2012.6513212"},{"key":"129_CR104","doi-asserted-by":"publisher","unstructured":"Supriya S, Siuly S, Wang H, Zhang Y. Weighted complex network based framework for epilepsy detection from EEG signals. Modelling and analysis of active biopotential signals in healthcare, volume 1. 2020. https:\/\/doi.org\/10.1088\/978-0-7503-3279-8ch3.","DOI":"10.1088\/978-0-7503-3279-8ch3"},{"key":"129_CR105","doi-asserted-by":"crossref","unstructured":"Liu M, Meng Q, Zhang Q, Wang D, Zhang H. The feature extraction method of EEG signals based on transition network. Advances in neural networks\u2014ISNN 2017 lecture notes in computer science; 2017. p. 491\u2013497.","DOI":"10.1007\/978-3-319-59081-3_57"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-020-00129-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-020-00129-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-020-00129-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T14:48:22Z","timestamp":1698245302000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-020-00129-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":105,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["129"],"URL":"https:\/\/doi.org\/10.1007\/s13755-020-00129-1","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"5 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"33"}}