{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:58:57Z","timestamp":1762955937275,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,8,4]],"date-time":"2018-08-04T00:00:00Z","timestamp":1533340800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61501283","61701279","61701270","61401259"],"award-info":[{"award-number":["61501283","61701279","61701270","61401259"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shandong Province (CN)","award":["ZR2015PF012"],"award-info":[{"award-number":["ZR2015PF012"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2017PF006"],"award-info":[{"award-number":["ZR2017PF006"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2015M582129","2015M582128"],"award-info":[{"award-number":["2015M582129","2015M582128"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1007\/s11517-018-1881-5","type":"journal-article","created":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T22:02:15Z","timestamp":1533333735000},"page":"205-219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Automatic seizure detection based on kernel robust probabilistic collaborative representation"],"prefix":"10.1007","volume":"57","author":[{"given":"Zuyi","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangzhou","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shasha","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Leng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1342-3374","authenticated-orcid":false,"given":"Qi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,8,4]]},"reference":[{"issue":"4","key":"1881_CR1","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1111\/j.0013-9580.2005.66104.x","volume":"46","author":"RS Fisher","year":"2005","unstructured":"Fisher RS, WVE B, Blume W, Elger C, Genton P, Lee P, Engel J (2005) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46(4):470\u2013472. \n                    https:\/\/doi.org\/10.1111\/j.0013-9580.2005.66104.x","journal-title":"Epilepsia"},{"key":"1881_CR2","doi-asserted-by":"crossref","unstructured":"Behnam M, Pourghassem H (2017) Seizure-specific wavelet (Seizlet) design for epileptic seizure detection using CorrEntropy ellipse features based on seizure modulus maximas patterns. J Neurosci Methods:27684\u201327107","DOI":"10.1016\/j.jneumeth.2016.10.011"},{"key":"1881_CR3","doi-asserted-by":"crossref","unstructured":"Zhang T, Chen W, Li M (2017) AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier. Biomed Signal Process:31550\u201331559","DOI":"10.1016\/j.bspc.2016.10.001"},{"key":"1881_CR4","doi-asserted-by":"crossref","unstructured":"Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl-Based Syst:8885\u20138896","DOI":"10.1016\/j.knosys.2015.08.004"},{"issue":"5","key":"1881_CR5","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/0013-4694(82)90038-4","volume":"54","author":"J Gotman","year":"1982","unstructured":"Gotman J (1982) Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol 54(5):530\u2013540. \n                    https:\/\/doi.org\/10.1016\/0013-4694(82)90038-4","journal-title":"Electroencephalogr Clin Neurophysiol"},{"issue":"9","key":"1881_CR6","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1016\/j.clinph.2009.07.002","volume":"120","author":"A Aarabi","year":"2009","unstructured":"Aarabi A, Fazel-Rezai R, Aghakhani Y (2009) A fuzzy rule-based system for epileptic seizure detection in intracranial EEG. Clin Neurophysiol 120(9):1648\u20131657. \n                    https:\/\/doi.org\/10.1016\/j.clinph.2009.07.002","journal-title":"Clin Neurophysiol"},{"issue":"10","key":"1881_CR7","doi-asserted-by":"publisher","first-page":"13475","DOI":"10.1016\/j.eswa.2011.04.149","volume":"38","author":"U Orhan","year":"2011","unstructured":"Orhan U, Hekim M, Ozer M (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38(10):13475\u201313481. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2011.04.149","journal-title":"Expert Syst Appl"},{"issue":"05","key":"1881_CR8","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1142\/S0129065711002912","volume":"21","author":"UR Acharya","year":"2011","unstructured":"Acharya UR, Sree SV, Suri JS (2011) Automatic detection of epileptic EEG signals using higher order cumulant features. Int J Neural Syst 21(05):403\u2013414. \n                    https:\/\/doi.org\/10.1142\/S0129065711002912","journal-title":"Int J Neural Syst"},{"issue":"3","key":"1881_CR9","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TITB.2006.884369","volume":"11","author":"V Srinivasan","year":"2007","unstructured":"Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 11(3):288\u2013295. \n                    https:\/\/doi.org\/10.1109\/TITB.2006.884369","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"2","key":"1881_CR10","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1016\/j.eswa.2009.05.078","volume":"37","author":"ED \u00dcbeyli","year":"2010","unstructured":"\u00dcbeyli ED (2010) Lyapunov exponents\/probabilistic neural networks for analysis of EEG signals. Expert Syst Appl 37(2):985\u2013992. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2009.05.078","journal-title":"Expert Syst Appl"},{"key":"1881_CR11","doi-asserted-by":"crossref","unstructured":"Yuan Q, Zhou W, Zhang L, Zhang F, Xu F, Leng Y, Wei D, Chen M (2017) Epileptic seizure detection based on imbalanced classification and wavelet packet transform. Seizure:5099\u20135108","DOI":"10.1016\/j.seizure.2017.05.018"},{"issue":"7","key":"1881_CR12","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.compbiomed.2004.05.001","volume":"35","author":"MK K\u0131ym\u0131k","year":"2005","unstructured":"K\u0131ym\u0131k MK, G\u00fcler IN, Dizib\u00fcy\u00fck A, Ak\u0131n M (2005) Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Med 35(7):603\u2013616. \n                    https:\/\/doi.org\/10.1016\/j.compbiomed.2004.05.001","journal-title":"Comput Biol Med"},{"issue":"5","key":"1881_CR13","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","volume":"13","author":"AT Tzallas","year":"2009","unstructured":"Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time\u2013frequency analysis. IEEE Trans Inf Technol Biomed 13(5):703\u2013710. \n                    https:\/\/doi.org\/10.1109\/TITB.2009.2017939","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"4","key":"1881_CR14","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1016\/j.eswa.2006.02.005","volume":"32","author":"A Subasi","year":"2007","unstructured":"Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084\u20131093. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2006.02.005","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1881_CR15","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210\u2013227. \n                    https:\/\/doi.org\/10.1109\/TPAMI.2008.79","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1881_CR16","unstructured":"Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: IEEE Int Conf Computer Vision. p. 471\u2013478"},{"issue":"02","key":"1881_CR17","doi-asserted-by":"publisher","first-page":"1550003","DOI":"10.1142\/S0129065715500033","volume":"25","author":"S Yuan","year":"2015","unstructured":"Yuan S, Zhou W, Yuan Q, Li X, Wu Q, Zhao X, Wang J (2015) Kernel collaborative representation-based automatic seizure detection in intracranial EEG. Int J Neural Syst 25(02):1550003. \n                    https:\/\/doi.org\/10.1142\/S0129065715500033","journal-title":"Int J Neural Syst"},{"issue":"04","key":"1881_CR18","doi-asserted-by":"publisher","first-page":"1450015","DOI":"10.1142\/S0129065714500154","volume":"24","author":"Q Yuan","year":"2014","unstructured":"Yuan Q, Zhou W, Yuan S, Li X, Wang J, Jia G (2014) Epileptic EEG classification based on kernel sparse representation. Int J Neural Syst 24(04):1450015. \n                    https:\/\/doi.org\/10.1142\/S0129065714500154","journal-title":"Int J Neural Syst"},{"key":"1881_CR19","doi-asserted-by":"crossref","unstructured":"Cai S, Zhang L, Zuo W, Feng X (2016) A probabilistic collaborative representation based approach for pattern classification. In: IEEE Int Conf on Computer Vision and Pattern Recognition. p. 2950\u20132959","DOI":"10.1109\/CVPR.2016.322"},{"issue":"6","key":"1881_CR20","doi-asserted-by":"publisher","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 (2001) 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 64(6):061907. \n                    https:\/\/doi.org\/10.1103\/PhysRevE.64.061907","journal-title":"Phys Rev E"},{"issue":"3","key":"1881_CR21","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.physd.2004.02.013","volume":"194","author":"T Maiwald","year":"2004","unstructured":"Maiwald T (2004) Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D 194(3):357\u2013368","journal-title":"Physica D"},{"issue":"2","key":"1881_CR22","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/51.376754","volume":"14","author":"T Kalayci","year":"1995","unstructured":"Kalayci T, Ozdamar O (1995) Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Eng Med Biol Mag 14(2):160\u2013166. \n                    https:\/\/doi.org\/10.1109\/51.376754","journal-title":"IEEE Eng Med Biol Mag"},{"issue":"5","key":"1881_CR23","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1016\/S1388-2457(03)00035-X","volume":"114","author":"Y Khan","year":"2003","unstructured":"Khan Y, Gotman J (2003) Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin Neurophysiol 114(5):898\u2013908. \n                    https:\/\/doi.org\/10.1016\/S1388-2457(03)00035-X","journal-title":"Clin Neurophysiol"},{"issue":"4","key":"1881_CR24","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1109\/TNSRE.2011.2157525","volume":"19","author":"KK Majumdar","year":"2011","unstructured":"Majumdar KK, Vardhan P (2011) Automatic seizure detection in ECoG by differential operator and windowed variance. IEEE Trans Neural Syst Rehab Eng 19(4):356\u2013365. \n                    https:\/\/doi.org\/10.1109\/TNSRE.2011.2157525","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"issue":"1","key":"1881_CR25","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compbiomed.2011.10.010","volume":"42","author":"K Majumdar","year":"2012","unstructured":"Majumdar K (2012) Differential operator in seizure detection. Comput Biol Med 42(1):70\u201374. \n                    https:\/\/doi.org\/10.1016\/j.compbiomed.2011.10.010","journal-title":"Comput Biol Med"},{"issue":"1","key":"1881_CR26","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s11063-014-9403-4","volume":"43","author":"Q Liu","year":"2016","unstructured":"Liu Q (2016) Kernel local sparse representation based classifier. Neural Process Lett 43(1):85\u201395. \n                    https:\/\/doi.org\/10.1007\/s11063-014-9403-4","journal-title":"Neural Process Lett"},{"key":"1881_CR27","doi-asserted-by":"publisher","unstructured":"Yang S, Han Y, Zhang X (2012) A sparse kernel representation method for image classification. In: (IJCNN). p. 1\u20137, DOI: \n                    https:\/\/doi.org\/10.1007\/s00253-018-9238-4","DOI":"10.1007\/s00253-018-9238-4"},{"issue":"3","key":"1881_CR28","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.clinph.2010.06.034","volume":"122","author":"A Temko","year":"2011","unstructured":"Temko A, Thomas E, Marnane W, Lightbody G, Boylan G (2011) EEG-based neonatal seizure detection with support vector machines. Clin Neurophysiol 122(3):464\u2013473. \n                    https:\/\/doi.org\/10.1016\/j.clinph.2010.06.034","journal-title":"Clin Neurophysiol"},{"key":"1881_CR29","doi-asserted-by":"publisher","unstructured":"Raghunathan S, Jaitli A, Irazoqui PP (2011) Multistage seizure detection techniques optimized for low-power hardware platforms. Epilepsy Behav:22S61\u201322S68. \n                    https:\/\/doi.org\/10.1016\/j.yebeh.2011.09.008","DOI":"10.1016\/j.yebeh.2011.09.008"},{"issue":"1","key":"1881_CR30","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1179\/016164104773026534","volume":"26","author":"VP Nigam","year":"2004","unstructured":"Nigam VP, Graupe D (2004) A neural-network-based detection of epilepsy. Neurol Res 26(1):55\u201360","journal-title":"Neurol Res"},{"key":"1881_CR31","doi-asserted-by":"crossref","unstructured":"Guo L, Rivero D, Seoane JA, Pazos A (2009) Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the first ACM\/SIGEVO Summit on Genetic and Evolutionary Computation: ACM. p. 177\u2013184","DOI":"10.1145\/1543834.1543860"},{"issue":"6","key":"1881_CR32","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1055\/s-0038-1634122","volume":"45","author":"A Tzallas","year":"2006","unstructured":"Tzallas A, Karvelis P, Katsis C, Fotiadis D, Giannopoulos S, Konitsiotis S (2006) A method for classification of transient events in EEG recordings: application to epilepsy diagnosis. Meth Inf Med 45(6):610\u2013621","journal-title":"Meth Inf Med"},{"issue":"06","key":"1881_CR33","doi-asserted-by":"publisher","first-page":"1350028","DOI":"10.1142\/S0129065713500287","volume":"23","author":"Y Wang","year":"2013","unstructured":"Wang Y, Zhou W, Yuan Q, Li X, Meng Q, Zhao X, Wang J (2013) Comparison of ictal and interictal EEG signals using fractal features. Int J Neural Syst 23(06):1350028. \n                    https:\/\/doi.org\/10.1142\/S0129065713500287","journal-title":"Int J Neural Syst"},{"issue":"1","key":"1881_CR34","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.eplepsyres.2011.04.013","volume":"96","author":"Q Yuan","year":"2011","unstructured":"Yuan Q, Zhou W, Li S, Cai D (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96(1):29\u201338","journal-title":"Epilepsy Res"},{"issue":"2","key":"1881_CR35","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1016\/j.eswa.2007.12.065","volume":"36","author":"H Ocak","year":"2009","unstructured":"Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027\u20132036. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2007.12.065","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1881_CR36","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.jneumeth.2010.05.020","volume":"191","author":"L Guo","year":"2010","unstructured":"Guo L, Rivero D, Dorado J, Rabunal JR, Pazos A (2010) Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J Neurosci Methods 191(1):101\u2013109. \n                    https:\/\/doi.org\/10.1016\/j.jneumeth.2010.05.020","journal-title":"J Neurosci Methods"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11517-018-1881-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-018-1881-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-018-1881-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:43:54Z","timestamp":1568940234000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11517-018-1881-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,4]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["1881"],"URL":"https:\/\/doi.org\/10.1007\/s11517-018-1881-5","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2018,8,4]]},"assertion":[{"value":"3 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}