{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T03:54:38Z","timestamp":1754020478583,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2017,9,2]],"date-time":"2017-09-02T00:00:00Z","timestamp":1504310400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Science and Technology Project of Guangdong Province","award":["2016A010101020","2016A010101022","2016A010101021"],"award-info":[{"award-number":["2016A010101020","2016A010101022","2016A010101021"]}]},{"name":"Science and Technology Project of Zhanjiang City","award":["2016B01118"],"award-info":[{"award-number":["2016B01118"]}]},{"name":"Research Funds of Guangdong Medical University","award":["M2015031"],"award-info":[{"award-number":["M2015031"]}]},{"name":"Research Funds of Guangdong Medical University","award":["M2015029"],"award-info":[{"award-number":["M2015029"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2019,2]]},"DOI":"10.1007\/s13042-017-0716-2","type":"journal-article","created":{"date-parts":[[2017,9,2]],"date-time":"2017-09-02T04:55:43Z","timestamp":1504328143000},"page":"311-323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A novel framework based on biclustering for automatic epileptic seizure detection"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2310-027X","authenticated-orcid":false,"given":"Qin","family":"Lin","sequence":"first","affiliation":[]},{"given":"Shuqun","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Cuihong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Wencheng","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Jiaqian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Huai-Ling","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Xue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,9,2]]},"reference":[{"issue":"10","key":"716_CR1","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1111\/j.1528-1167.2005.00273_4.x","volume":"46","author":"RS Fisher","year":"2005","unstructured":"Fisher RS, Boas WVE, Blume W et al (2005) Response: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46(10):1701\u20131702","journal-title":"Epilepsia"},{"key":"716_CR2","unstructured":"WHO. Media Centre, Epilepsy, Fact Sheet. \n                    http:\/\/www.who.int\/mediacentre\/factsheets\/fs999\/en\/\n                    \n                  . Accessed 8 Oct 2016"},{"issue":"1","key":"716_CR3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1053\/seiz.1999.0353","volume":"9","author":"N Ellis","year":"2000","unstructured":"Ellis N, Upton D, Thompson P (2000) Epilepsy and the family: a review of current literature. Seizure J Br Epilepsy Assoc 9(1):22\u201330","journal-title":"Seizure J Br Epilepsy Assoc"},{"issue":"10","key":"716_CR4","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1016\/S1388-2457(99)00099-1","volume":"110","author":"CJ Stam","year":"1999","unstructured":"Stam CJ, Pijn JPM, Suffczynski P et al (1999) Dynamics of the human alpha rhythm: evidence for non-linearity? Clin Neurophysiol 110(10):1801\u20131813","journal-title":"Clin Neurophysiol"},{"key":"716_CR5","unstructured":"Zhao H, Guo X, Wang M et al (2015) Analyze EEG signals with extreme learning machine based on PMIS feature selection. Int J Mach Learn Cybern 1\u20137"},{"issue":"1","key":"716_CR6","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/0013-4694(92)90179-L","volume":"82","author":"A Liu","year":"1992","unstructured":"Liu A, Hahn JS, Heldt GP et al (1992) Detection of neonatal seizures through computerized EEG analysis. Electroencephalogr Clin Neurophysiol 82(1):30\u201337","journal-title":"Electroencephalogr Clin Neurophysiol"},{"issue":"6","key":"716_CR7","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s10916-005-9001-0","volume":"30","author":"A Alkan","year":"2007","unstructured":"Alkan A, Kiymik MK (2007) Comparison of AR and Welch methods in epileptic seizure detection. J Med Syst 30(6):413\u2013419","journal-title":"J Med Syst"},{"issue":"1","key":"716_CR8","doi-asserted-by":"publisher","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 (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123(1):69\u201387","journal-title":"J Neurosci Methods"},{"issue":"2","key":"716_CR9","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 Systems Appl 37(2):985\u2013992","journal-title":"Expert Systems Appl"},{"issue":"2","key":"716_CR10","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/0013-4694(95)00071-6","volume":"95","author":"K Lehnertz","year":"1995","unstructured":"Lehnertz K, Elger CE (1995) Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr Clin Neurophysiol 95(2):108\u2013117","journal-title":"Electroencephalogr Clin Neurophysiol"},{"issue":"2","key":"716_CR11","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.jneumeth.2012.07.003","volume":"210","author":"Y Song","year":"2012","unstructured":"Song Y, Crowcroft J, Zhang J (2012) Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Methods 210(2):132\u2013146","journal-title":"J Neurosci Methods"},{"key":"716_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1155\/2008\/293056","volume":"2008","author":"RB Pachori","year":"2008","unstructured":"Pachori RB (2008) Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res Lett Signal Process 2008:5","journal-title":"Res Lett Signal Process"},{"issue":"10","key":"716_CR13","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1109\/10.720198","volume":"45","author":"AB Geva","year":"1998","unstructured":"Geva AB, Kerem DH (1998) Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering. IEEE Trans Biomed Eng 45(10):1205\u20131216","journal-title":"IEEE Trans Biomed Eng"},{"issue":"10","key":"716_CR14","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","journal-title":"Expert Syst Appl"},{"key":"716_CR15","doi-asserted-by":"crossref","unstructured":"Sommer D, Golz M (2001) Clustering of EEG-Segments Using Hierarchical Agglomerative Methods and Self-Organizing Maps. In: Proceedings of the 2001 International Conference on Artificial Neural Networks, Vienna, Austria, pp\u00a0642\u2013649","DOI":"10.1007\/3-540-44668-0_90"},{"issue":"1","key":"716_CR16","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1016\/j.eswa.2011.07.106","volume":"39","author":"WY Hsu","year":"2012","unstructured":"Hsu WY (2012) Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification. Expert Syst Appl Int J 39(1):1055\u20131061","journal-title":"Expert Syst Appl Int J"},{"issue":"5","key":"716_CR17","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1109\/TBME.2012.2237399","volume":"60","author":"AS Zandi","year":"2013","unstructured":"Zandi AS, Tafreshi R, Javidan M et al (2013) Predicting epileptic seizures in scalp EEG based on a variational bayesian Gaussian mixture model of zero-crossing intervals. IEEE Trans Biomed Eng 60(5):1401\u20131413","journal-title":"IEEE Trans Biomed Eng"},{"key":"716_CR18","unstructured":"Cheng Y, Church GM (2000) Biclustering of expression data. In: Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, San Diego, La Jolla, California, USA, pp.\u00a093\u2013103"},{"issue":"10","key":"716_CR19","doi-asserted-by":"publisher","first-page":"e111318","DOI":"10.1371\/journal.pone.0111318","volume":"9","author":"HC Chen","year":"2014","unstructured":"Chen HC, Zou W, Lu TP et al (2014) A composite model for subgroup identification and prediction via bicluster analysis. Plos One 9(10):e111318","journal-title":"Plos One"},{"issue":"3","key":"716_CR20","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1515\/jib-2011-175","volume":"8","author":"AV Carreiro","year":"2011","unstructured":"Carreiro AV, Anuncia\u00e7\u00e3o O, Carri\u00e7o JA et al (2011) Prognostic prediction through biclustering-based classification of clinical gene expression time series. J Integr Bioinform 8(3):175\u2013175","journal-title":"J Integr Bioinform"},{"key":"716_CR21","first-page":"1","volume-title":"Using rank-1 biclusters to classify microarray data","author":"N Asgarian","year":"2006","unstructured":"Asgarian N, Greiner R (2006) Using rank-1 biclusters to classify microarray data. Department of Computing Science, University of Alberta, Edmonton, pp\u00a01\u201310"},{"issue":"1","key":"716_CR22","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1063\/1.2817345","volume":"953","author":"S Busygin","year":"2007","unstructured":"Busygin S, Boyko N, Pardalos PM et al (2007) Biclustering EEG data from epileptic patients treated with vagus nerve stimulation. Data Min Syst Anal Optim Biomed 953(1):220\u2013231","journal-title":"Data Min Syst Anal Optim Biomed"},{"issue":"1\u20133","key":"716_CR23","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"337","key":"716_CR24","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1080\/01621459.1972.10481214","volume":"67","author":"JA Hartigan","year":"1972","unstructured":"Hartigan JA (1972) Direct Clustering of a Data Matrix. J Am Stat Assoc 67(337):123\u2013129","journal-title":"J Am Stat Assoc"},{"key":"716_CR25","unstructured":"Bendor A, Chor B, Karp R et al (2003) Discovering local structure in gene expression data: the order-preserving submatrix problem. In: Proceedings of the 6th Annual International Conference on Computational Biology, Washington, DC, USA, vol.\u00a010, no. 3\u20134, pp.\u00a049\u201357"},{"issue":"1","key":"716_CR26","first-page":"61","volume":"12","author":"L Lazzeroni","year":"2002","unstructured":"Lazzeroni L, Owen A (2002) Plaid models for gene expression data. Stat Sin 12(1):61\u201386","journal-title":"Stat Sin"},{"key":"716_CR27","doi-asserted-by":"crossref","unstructured":"de Castro PAD, de Fran\u00e7a FO, Ferreira HM et al (2007) Applying biclustering to text mining: an immune-inspired approach. Artif Immune Syst 83\u201394","DOI":"10.1007\/978-3-540-73922-7_8"},{"key":"716_CR28","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.engappai.2015.06.005","volume":"47","author":"B Wang","year":"2016","unstructured":"Wang B, Miao Y, Zhao H et al (2016) A biclustering-based method for market segmentation using customer pain points. Eng Appl Artif Intell 47:101\u2013109","journal-title":"Eng Appl Artif Intell"},{"key":"716_CR29","unstructured":"Inbarani H, Thangavel K (2010) A robust biclustering approach for effective web personalization. Visual analytics and interactive technologies: data, text and web mining applications, pp\u00a0186\u2013202"},{"issue":"1","key":"716_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/498121","volume":"2015","author":"Y Xue","year":"2015","unstructured":"Xue Y, Liu Z, Luo J et al (2015) Stock market trading rules discovery based on biclustering method. Math Probl Eng 2015(1):1\u201313","journal-title":"Math Probl Eng"},{"issue":"1","key":"716_CR31","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s10791-007-9038-4","volume":"11","author":"P Symeonidis","year":"2008","unstructured":"Symeonidis P, Nanopoulos A, Papadopoulos AN et al (2008) Nearest-biclusters collaborative filtering based on constant and coherent values. Inf Retr 11(1):51\u201375","journal-title":"Inf Retr"},{"issue":"1","key":"716_CR32","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/TCBB.2004.2","volume":"1","author":"SC Madeira","year":"2004","unstructured":"Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: a survey. IEEE\/ACM Trans Comput Biol Bioinform 1(1):24\u201345","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"9","key":"716_CR33","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1093\/bioinformatics\/btl060","volume":"22","author":"A Preli\u0107","year":"2006","unstructured":"Preli\u0107 A, Bleuler S, Zimmermann P et al (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9):1122\u20131129","journal-title":"Bioinformatics"},{"issue":"Suppl 1","key":"716_CR34","doi-asserted-by":"publisher","first-page":"S136","DOI":"10.1093\/bioinformatics\/18.suppl_1.S136","volume":"18","author":"A Tanay","year":"2002","unstructured":"Tanay A, Sharan R, Shamir R (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(Suppl 1):S136\u2013S144","journal-title":"Bioinformatics"},{"issue":"3","key":"716_CR35","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1109\/LGRS.2010.2087006","volume":"8","author":"U Kumar","year":"2011","unstructured":"Kumar U, Raja SK, Mukhopadhyay C et al (2011) Hybrid Bayesian classifier for improved classification accuracy. IEEE Geosci Remote Sens Lett 8(3):474\u2013477","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"4","key":"716_CR36","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/5326.897072","volume":"30","author":"GP Zhang","year":"2000","unstructured":"Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 30(4):451\u2013462","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"issue":"8","key":"716_CR37","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neucom.2013.11.009","volume":"133","author":"Y Kumar","year":"2014","unstructured":"Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133(8):271\u2013279","journal-title":"Neurocomputing"},{"issue":"3","key":"716_CR38","first-page":"249","volume":"31","author":"SB Kotsiantis","year":"2007","unstructured":"Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31(3):249\u2013268","journal-title":"Informatica"},{"issue":"16","key":"716_CR39","doi-asserted-by":"publisher","first-page":"2520","DOI":"10.1016\/j.neucom.2010.12.034","volume":"74","author":"XZ Wang","year":"2011","unstructured":"Wang XZ, Chen AX, Feng HM (2011) Upper integral network with extreme learning mechanism. Neurocomputing 74(16):2520\u20132525","journal-title":"Neurocomputing"},{"issue":"1\u20132","key":"716_CR40","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 et al (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96(1\u20132):29\u201338","journal-title":"Epilepsy Res"},{"issue":"6","key":"716_CR41","doi-asserted-by":"publisher","first-page":"0619071","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak RG, Lehnertz K, Mormann F et al (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\u00a064(6):0619071\u20130619078","journal-title":"Phys Rev E"},{"key":"716_CR42","doi-asserted-by":"crossref","unstructured":"Jahankhani P, Kodogiannis V, Revett K (2006) EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. IEEE Symposium on John Vincent Atanasoff International Modern Computing, Sofia, pp\u00a0120\u2013124","DOI":"10.1109\/JVA.2006.17"},{"issue":"2","key":"716_CR43","first-page":"1017","volume":"187","author":"K Polat","year":"2007","unstructured":"Polat K, G\u00fcne\u015f S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187(2):1017\u20131026","journal-title":"Appl Math Comput"},{"key":"716_CR44","doi-asserted-by":"crossref","unstructured":"Guo L, Rivero D, Seoane JA et al (2009) Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the 2nd Genetic and Evolutionary Computation Conference, Shanghai, China, pp.\u00a0177\u2013184","DOI":"10.1145\/1543834.1543860"},{"issue":"2","key":"716_CR45","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1016\/j.eswa.2007.11.017","volume":"36","author":"S Chandaka","year":"2009","unstructured":"Chandaka S, Chatterjee A, Munshi S (2009) Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst Appl Int J 36(2):1329\u20131336","journal-title":"Expert Syst Appl Int J"},{"issue":"1","key":"716_CR46","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eswa.2009.05.012","volume":"37","author":"ED \u00dcbeyli\u02d9","year":"2010","unstructured":"\u00dcbeyli\u02d9 ED (2010) Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Syst Appl 37(1):233\u2013239","journal-title":"Expert Syst Appl"},{"issue":"2","key":"716_CR47","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.cmpb.2013.11.014","volume":"113","author":"RB Pachori","year":"2014","unstructured":"Pachori RB, Patidar S (2014) Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed 113(2):pp\u00a0494\u2013502","journal-title":"Comput Methods Programs Biomed"},{"key":"716_CR48","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jneumeth.2015.05.026","volume":"243","author":"X Jie","year":"2015","unstructured":"Jie X, Li C, Li H et al (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18\u201325","journal-title":"J Neurosci Methods"},{"issue":"PA","key":"716_CR49","first-page":"383","volume":"175","author":"JL Song","year":"2015","unstructured":"Song JL, Hu W, Zhang R (2015) Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine. Neurocomputing 175(PA):383\u2013391","journal-title":"Neurocomputing"},{"key":"716_CR50","unstructured":"Yang J, Wang H, Wang W et al (2003) Enhanced Biclustering on Expression Data. In: Proceedings of the 3rd IEEE Symposium on Bioinformatics and Bioengineering, pp.\u00a0321\u2013327"},{"issue":"2","key":"716_CR51","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.sigpro.2011.08.005","volume":"92","author":"MT Akhtar","year":"2012","unstructured":"Akhtar MT, Mitsuhashi W, James CJ (2012) Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. Signal Process 92(2):401\u2013416","journal-title":"Signal Process"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-017-0716-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0716-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0716-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,27]],"date-time":"2019-04-27T02:43:02Z","timestamp":1556332982000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-017-0716-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,2]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,2]]}},"alternative-id":["716"],"URL":"https:\/\/doi.org\/10.1007\/s13042-017-0716-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2017,9,2]]},"assertion":[{"value":"28 November 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is no conflict of interests regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}