{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:11:52Z","timestamp":1769148712401,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T00:00:00Z","timestamp":1509062400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1007\/s13755-017-0028-7","type":"journal-article","created":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T18:02:01Z","timestamp":1509127321000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["An optimum allocation sampling based feature extraction scheme for distinguishing seizure and seizure-free EEG signals"],"prefix":"10.1007","volume":"5","author":[{"given":"Sachin","family":"Taran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varun","family":"Bajaj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siuly","family":"Siuly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,27]]},"reference":[{"key":"28_CR1","unstructured":"Global campaign against epilepsy. Programme for Neurological Diseases, Neuroscience (World Health Organization), International Bureau of Epilepsy, and International League against Epilepsy. Atlas: epilepsy care in the world. World Health Organization; 2005."},{"issue":"2","key":"28_CR2","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1093\/brain\/awl241","volume":"130","author":"F Mormann","year":"2006","unstructured":"Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain. 2006;130(2):314\u201333.","journal-title":"Brain"},{"issue":"9","key":"28_CR3","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/78.317850","volume":"42","author":"GC Ray","year":"1994","unstructured":"Ray GC. An algorithm to separate nonstationary part of a signal using mid-prediction filter. IEEE Trans Signal Process. 1994;42(9):2276\u20139.","journal-title":"IEEE Trans Signal Process."},{"issue":"2","key":"28_CR4","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/10.661266","volume":"45","author":"S Mukhopadhyay","year":"1998","unstructured":"Mukhopadhyay S, Ray GC. A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Trans Biomed Eng. 1998;45(2):180\u20137.","journal-title":"IEEE Trans Biomed Eng."},{"issue":"9971","key":"28_CR5","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1016\/S0140-6736(14)60456-6","volume":"385","author":"SL Mosh","year":"2015","unstructured":"Mosh SL, Perucca E, Ryvlin P, Tomson T. Epilepsy: new advances. Lancet. 2015;385(9971):884\u201398.","journal-title":"Lancet"},{"issue":"1","key":"28_CR6","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1186\/1475-925X-13-123","volume":"13","author":"L Duque-Muz","year":"2014","unstructured":"Duque-Muz L, Espinosa-Oviedo JJ, Castellanos-Dominguez CG. Identification and monitoring of brain activity based on stochastic relevance analysis of shorttime EEG rhythms. Biomed Eng Online. 2014;13(1):123.","journal-title":"Biomed Eng Online"},{"issue":"6","key":"28_CR7","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 AN. 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."},{"issue":"2","key":"28_CR8","first-page":"1017","volume":"187","author":"K Polat","year":"2007","unstructured":"Polat K, Gne S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput. 2007;187(2):1017\u201326.","journal-title":"Appl Math Comput."},{"issue":"02","key":"28_CR9","doi-asserted-by":"crossref","first-page":"1350011","DOI":"10.1142\/S0218348X13500114","volume":"21","author":"R Uthayakumar","year":"2013","unstructured":"Uthayakumar R, Easwaramoorthy D. Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals. 2013;21(02):1350011.","journal-title":"Fractals"},{"issue":"1","key":"28_CR10","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.jneumeth.2010.08.030","volume":"193","author":"L Guo","year":"2010","unstructured":"Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods. 2010;193(1):156\u201363.","journal-title":"J Neurosci Methods"},{"issue":"8","key":"28_CR11","doi-asserted-by":"crossref","first-page":"5661","DOI":"10.1016\/j.eswa.2010.02.045","volume":"37","author":"S Altunay","year":"2010","unstructured":"Altunay S, Telatar Z, Erogul O. Epileptic EEG detection using the linear prediction error energy. Expert Syst Appl. 2010;37(8):5661\u20135.","journal-title":"Expert Syst Appl."},{"key":"28_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2013.08.006","volume":"9","author":"V Joshi","year":"2014","unstructured":"Joshi V, Pachori RB, Vijesh A. Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control. 2014;9:1\u20135.","journal-title":"Biomed Signal Process Control"},{"issue":"3","key":"28_CR13","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.cmpb.2010.11.014","volume":"104","author":"S Siuly","year":"2011","unstructured":"Siuly S, Li Y, Wen PP. 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."},{"issue":"4","key":"28_CR14","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1016\/j.eswa.2009.10.036","volume":"37","author":"T Gandhi","year":"2010","unstructured":"Gandhi T, Panigrahi BK, Bhatia M, Anand S. Expert model for detection of epileptic activity in EEG signature. Expert Syst Appl. 2010;37(4):3513\u201320.","journal-title":"Expert Syst Appl."},{"issue":"2","key":"28_CR15","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.cmpb.2013.11.014","volume":"113","author":"RB Pachori","year":"2014","unstructured":"Pachori RB, Patidar S. Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed. 2014;113(2):494\u2013502.","journal-title":"Comput Methods Programs Biomed."},{"issue":"1","key":"28_CR16","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s13534-013-0084-0","volume":"3","author":"V Bajaj","year":"2013","unstructured":"Bajaj V, Pachori RB. Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed Eng Lett. 2013;3(1):17\u201321.","journal-title":"Biomed Eng Lett."},{"issue":"6","key":"28_CR17","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/TITB.2011.2181403","volume":"16","author":"V Bajaj","year":"2012","unstructured":"Bajaj V, Pachori RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed. 2012;16(6):1135\u201342.","journal-title":"IEEE Trans Inf Technol Biomed."},{"issue":"3","key":"28_CR18","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1016\/j.eswa.2014.08.030","volume":"42","author":"R Sharma","year":"2015","unstructured":"Sharma R, Pachori RB. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl. 2015;42(3):1106\u201317.","journal-title":"Expert Syst Appl."},{"key":"28_CR19","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.cmpb.2016.09.008","volume":"137","author":"AR Hassan","year":"2016","unstructured":"Hassan AR, Siuly S, Zhang Y. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed. 2016;137:247\u201359.","journal-title":"Comput Methods Programs Biomed."},{"key":"28_CR20","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.bspc.2017.01.001","volume":"34","author":"S Patidar","year":"2017","unstructured":"Patidar S, Panigrahi T. Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Process Control. 2017;34:74\u201380.","journal-title":"Biomed Signal Process Control"},{"issue":"02","key":"28_CR21","doi-asserted-by":"crossref","first-page":"1250002","DOI":"10.1142\/S0129065712500025","volume":"22","author":"UR Acharya","year":"2012","unstructured":"Acharya UR, Sree SV, Ang PCA, Yanti R, Suri JS. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst. 2012;22(02):1250002.","journal-title":"Int J Neural Syst."},{"issue":"3","key":"28_CR22","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TITB.2006.884369","volume":"11","author":"V Srinivasan","year":"2007","unstructured":"Srinivasan V, Eswaran C, Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed. 2007;11(3):288\u201395.","journal-title":"IEEE Trans Inf Technol Biomed."},{"issue":"2","key":"28_CR23","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/0013-4694(95)00071-6","volume":"95","author":"K Lehnertz","year":"1995","unstructured":"Lehnertz K, Elger CE. Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr Clin Neurophysiol. 1995;95(2):108\u201317.","journal-title":"Electroencephalogr Clin Neurophysiol."},{"issue":"2","key":"28_CR24","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1016\/j.eswa.2009.05.078","volume":"37","author":"ED Beyli","year":"2010","unstructured":"Beyli ED. Lyapunov exponents\/probabilistic neural networks for analysis of EEG signals. Expert Syst Appl. 2010;37(2):985\u201392.","journal-title":"Expert Syst Appl."},{"key":"28_CR25","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.engappai.2014.05.011","volume":"34","author":"Y Li","year":"2014","unstructured":"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."},{"issue":"4","key":"28_CR26","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1007\/s00521-014-1753-3","volume":"26","author":"S Siuly","year":"2015","unstructured":"Siuly S, Li Y. Discriminating the brain activities for brain\u2013computer interface applications through the optimal allocation-based approach. Neural Comput Appl. 2015;26(4):799\u2013811.","journal-title":"Neural Comput Appl."},{"key":"28_CR27","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.measurement.2016.02.059","volume":"86","author":"S Siuly","year":"2016","unstructured":"Siuly S, Wang H, Zhang Y. Detection of motor imagery EEG signals employing Naive Bayes based learning process. Measurement. 2016;86:148\u201358.","journal-title":"Measurement"},{"key":"28_CR28","unstructured":"Kvedalen E. Signal processing using the Teager Energy Operator and other nonlinear operators. Master, University of Oslo Department of Informatics, p. 21 (2003)."},{"key":"28_CR29","unstructured":"Zhou G, Hansen JH, Kaiser JF. Classification of speech under stress based on features derived from the nonlinear Teager energy operator. In: Proceedings of the 1998 IEEE international conference on acoustics, speech and signal processing, May 1998, vol. 1, p. 549\u2013552 (1998)."},{"issue":"6","key":"28_CR30","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1016\/j.jsv.2013.11.003","volume":"333","author":"M Cao","year":"2014","unstructured":"Cao M, Xu W, Ostachowicz W, Su Z. Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature. J Sound Vib. 2014;333(6):1543\u201353.","journal-title":"J Sound Vib."},{"issue":"11","key":"28_CR31","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/97.542159","volume":"3","author":"B Santhanam","year":"1996","unstructured":"Santhanam B, Maragos P. Energy demodulation of two-component AM\u2013FM signal mixtures. IEEE Signal Process Lett. 1996;3(11):294\u20138.","journal-title":"IEEE Signal Process Lett."},{"issue":"6","key":"28_CR32","doi-asserted-by":"crossref","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. 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. 2001;64(6):061907.","journal-title":"Phys Rev E"},{"key":"28_CR33","unstructured":"Sample Size Calculator. \n                        https:\/\/www.surveysystem.com\/sscalc.htm\n                        \n                    ."},{"key":"28_CR34","volume-title":"Sampling techniques","author":"WG Cochran","year":"1977","unstructured":"Cochran WG. Sampling techniques. 3rd ed. New York: Wiley; 1977.","edition":"3"},{"issue":"10","key":"28_CR35","doi-asserted-by":"crossref","first-page":"3024","DOI":"10.1109\/78.277799","volume":"41","author":"P Maragos","year":"1993","unstructured":"Maragos P, Kaiser JF, Quatieri TF. Energy separation in signal modulations with application to speech analysis. IEEE Trans Signal Process. 1993;41(10):3024\u201351.","journal-title":"IEEE Trans Signal Process."},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Boudraa AO, Cexus JC, Salzenstein F, Guillon L. IF estimation using empirical mode decomposition and nonlinear Teager energy operator. In: First international symposium on control, communications and signal processing, p. 45\u201348 (2004).","DOI":"10.1109\/ISCCSP.2004.1296215"},{"key":"28_CR37","volume-title":"Intro stats","author":"RD Veaux De","year":"2008","unstructured":"De Veaux RD, Velleman PF, Bock DE. Intro stats. 3rd ed. Boston: Pearson Addison Wesley; 2008.","edition":"3"},{"issue":"3","key":"28_CR38","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9(3):293\u2013300.","journal-title":"Neural Process Lett."},{"key":"28_CR39","unstructured":"Li Y, Wen P. Analysis and classification of EEG signals using a hybrid clustering technique. In: 2010 IEEE\/ICME international conference on complex medical engineering (CME), July 2010, p. 34\u201339 (2010)."}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13755-017-0028-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-017-0028-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-017-0028-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,12,28]],"date-time":"2017-12-28T14:34:10Z","timestamp":1514471650000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13755-017-0028-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,27]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["28"],"URL":"https:\/\/doi.org\/10.1007\/s13755-017-0028-7","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,27]]},"article-number":"7"}}