{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T07:30:36Z","timestamp":1750750236126},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s11042-021-11035-3","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T09:02:45Z","timestamp":1625562165000},"page":"19795-19811","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identification of inter-ictal activity in novel data by bagged prediction method using beta and gamma waves"],"prefix":"10.1007","volume":"81","author":[{"given":"Arshpreet","family":"Kaur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinod","family":"Puri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karan","family":"Verma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amol P","family":"Bhondekar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kumar","family":"Shashvat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,30]]},"reference":[{"key":"11035_CR1","doi-asserted-by":"publisher","first-page":"17849","DOI":"10.1007\/s11042-021-10597-6","volume":"80","author":"MBQ Aayesha","year":"2021","unstructured":"Aayesha MBQ, Afzaal M, Qureshi MS, Fayaz M (2021) Machine learning-based EEG signals classification model for epileptic seizure detection. Multimed Tools Appl 80:17849\u201317877. https:\/\/doi.org\/10.1007\/s11042-021-10597-6","journal-title":"Multimed Tools Appl"},{"issue":"10","key":"11035_CR2","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1109\/TBME.2006.889772","volume":"54","author":"HM Al-Angari","year":"2007","unstructured":"Al-Angari HM, Sahakian AV (2007) Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans Biomed Eng 54(10):1900\u20131904. https:\/\/doi.org\/10.1109\/TBME.2006.889772","journal-title":"IEEE Trans Biomed Eng"},{"issue":"3","key":"11035_CR3","doi-asserted-by":"publisher","first-page":"263","DOI":"10.4103\/0972-2327.160093","volume":"18","author":"S Amudhan","year":"2015","unstructured":"Amudhan S, Gururaj G, Satishchandra P (2015) Epilepsy in India I: epidemiology and public health. Ann Indian Acad Neurol 18(3):263\u2013277 10\/20 System Positioning Manual, Trans Cranial Technol., Hong Kong, 2012","journal-title":"Ann Indian Acad Neurol"},{"key":"11035_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-009-9795-x","volume-title":"Log energy entropy-based EEG classification with multilayer neural networks in seizure","author":"S Ayd\u0131n","year":"2009","unstructured":"Ayd\u0131n S, et al. (2009) Log energy entropy-based EEG classification with multilayer neural networks in seizure"},{"key":"11035_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"issue":"1","key":"11035_CR6","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1177\/155005940503600106","volume":"36","author":"N Burioka","year":"2017","unstructured":"Burioka N, Miyata M, Corn\u00e9lissen G, Halberg F, Takeshima T, Kaplan DT, Shimizu E (2017) Approximate entropy in the electroencephalogram during wake and sleep. Clinical EEG and Neuroscience 36(1):21\u201324. https:\/\/doi.org\/10.1177\/155005940503600106","journal-title":"Clinical EEG and Neuroscience"},{"key":"11035_CR7","doi-asserted-by":"publisher","unstructured":"Comparison of classification models using entropy based features from sub-bands of EEG. Kaur, Arshpreet, et al. (2020). 2, s.l. : International Information and Engineering Technology Association, 4 1, 2020, Traitement du Signal 37: 279\u2013289. https:\/\/doi.org\/10.18280\/ts.370214.","DOI":"10.18280\/ts.370214"},{"issue":"2","key":"11035_CR8","first-page":"68","volume":"3","author":"S Divya","year":"2015","unstructured":"Divya S (2015) Classification of EEG signal for epileptic seizure detection using EMD and ELM. International Journal for Trends in Engineering and Technology 3(2):68\u201374","journal-title":"International Journal for Trends in Engineering and Technology"},{"issue":"2","key":"11035_CR9","first-page":"68","volume":"3","author":"S Divya","year":"2015","unstructured":"Divya S (2015) Classification of EEG signal for epileptic seizure detection using EMD and ELM. International Journal for Trends in Engineering and Technology 3(2):68\u201374","journal-title":"International Journal for Trends in Engineering and Technology"},{"key":"11035_CR10","unstructured":"Gotman J (1985) Seizure recognition and analysis. In: J. Gotman, J.R. lves and P. Gloor (Eds.), Long-Term Monitoring in Epilepsy. Electroenceph.clin. Neurophysiol., Suppl. 37. Elsevier, Amsterdam: 133\u2013145."},{"issue":"1","key":"11035_CR11","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3906\/elk-1306-164","volume":"24","author":"M Hekim","year":"2016","unstructured":"Hekim M (2016) The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system. Turkish J Electr Eng Comput Sci 24(1):285\u2013297","journal-title":"Turkish J Electr Eng Comput Sci"},{"key":"11035_CR12","doi-asserted-by":"publisher","unstructured":"Hekim M (2016) The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system. https:\/\/doi.org\/10.3906\/elk-1306-164","DOI":"10.3906\/elk-1306-164"},{"key":"11035_CR13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.bspc.2017.01.005","volume":"34","author":"AK Jaiswal","year":"2017","unstructured":"Jaiswal AK, Banka H (2017) Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 34:81\u201392","journal-title":"Biomed Signal Process Control"},{"key":"11035_CR14","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.bspc.2017.01.005","volume":"34","author":"AK Jaiswal","year":"2017","unstructured":"Jaiswal AK, Banka H (2017) Biomedical signal processing and control local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 34:81\u201392. https:\/\/doi.org\/10.1016\/j.bspc.2017.01.005","journal-title":"Biomed Signal Process Control"},{"key":"11035_CR15","doi-asserted-by":"publisher","unstructured":"Jukic S, Saracevic M, Subasi A, Kevric J (2020) Comparison of ensemble machine learning methods for automated classification of focal and non-focal epileptic EEG signals. Mathematics 8(9). https:\/\/doi.org\/10.3390\/math8091481","DOI":"10.3390\/math8091481"},{"key":"11035_CR16","doi-asserted-by":"crossref","unstructured":"Kane N, Acharya J, Benickzy S, Caboclo L, Finnigan S, Kaplan PW, et al. (2017) A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Clin Neurophysiol Pract","DOI":"10.1016\/j.cnp.2017.07.002"},{"key":"11035_CR17","doi-asserted-by":"crossref","unstructured":"Kaur A, Verma K, Bhondekar AP, Shashvat K Implementation of bagged SVM ensemble model for classification of epileptic states using EEG. Current Pharmaceutical Biotechnology (Bentham Science Publishers Ltd.) 20(9) (7 2019):755\u2013765","DOI":"10.2174\/1389201020666190618112715"},{"issue":"7","key":"11035_CR18","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s11760-012-0362-9","volume":"8","author":"Y Kumar","year":"2012","unstructured":"Kumar Y, Dewal ML, Anand RS (2012) Epileptic seizures detection in Eeg using Dwt-based apen and artificial neural network. Signal Image Video Process 8(7):1323\u20131334","journal-title":"Signal Image Video Process"},{"key":"11035_CR19","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. 133:271\u2013279","journal-title":"Neurocomputing."},{"key":"11035_CR20","doi-asserted-by":"publisher","unstructured":"Kumar Y, Dewal ML, Anand RS Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133:271\u2013279. https:\/\/doi.org\/10.1016\/j.neucom.2013.11.009","DOI":"10.1016\/j.neucom.2013.11.009"},{"key":"11035_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnhum.2013.00138","volume":"7","author":"SD Muthukumaraswamy","year":"2013","unstructured":"Muthukumaraswamy SD (2013) High-frequency brain activity and muscle artifacts in MEG\/EEG: a review and recommendations. Front Hum Neurosci 7:1\u201311","journal-title":"Front Hum Neurosci"},{"key":"11035_CR22","unstructured":"Panych LP, Wada JA (1990) Computer applications in data analysis. In:J.A. Wada and R.J. EUingson (Eds.), Clinical Neurophysiology of Epilepsy. EEG Handbook (Rev. Ser.). Amsterdam, Elsevier:361\u2013385."},{"key":"11035_CR23","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.bspc.2017.01.001","volume":"34","author":"T Patidar","year":"2017","unstructured":"Patidar T, Panigrahi (2017) Detection of epileptic seizure using kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomedical Signal Processing and Control 34:74\u201380","journal-title":"Biomedical Signal Processing and Control"},{"key":"11035_CR24","doi-asserted-by":"crossref","unstructured":"Pradhan, N, P K Sadasivan, and G R Arunodaya. \"Detection of seizure activity in EEG by an artificial neural network: a preliminary study.\" 1996, 303\u2013313","DOI":"10.1006\/cbmr.1996.0022"},{"key":"11035_CR25","doi-asserted-by":"crossref","unstructured":"Puspita JW, Soemarno G, Jaya AI, Soewono (2018). E. Interictal Epileptiform discharges (IEDs) classification in EEG data of epilepsy patients. J Phys Conf Ser. 943.","DOI":"10.1088\/1742-6596\/943\/1\/012030"},{"key":"11035_CR26","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.eswa.2017.07.029","volume":"89","author":"S Raghu","year":"2017","unstructured":"Raghu S, Sriraam N (2017) Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures. Expert Syst Appl 89:205\u2013221","journal-title":"Expert Syst Appl"},{"issue":"0","key":"11035_CR27","first-page":"1","volume":"0","author":"M Saiby","year":"2018","unstructured":"Saiby M, Kajri S, Sharmila A, Mahalakshmi P (2018) A case study on discrete wavelet transform based Hurst exponent for a case study on discrete wavelet transform based Hurst exponent for epilepsy detection. J Med Eng Technol 0(0):1\u20139","journal-title":"J Med Eng Technol"},{"key":"11035_CR28","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon CE (1948) Mathematical theory of communication. Bell Syst. Tech. J 27:379\u2013423, 623\u2013656","journal-title":"Bell Syst. Tech. J"},{"key":"11035_CR29","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1016\/j.eswa.2014.08.030","volume":"42","author":"R Sharma","year":"2015","unstructured":"Sharma R, Pachori RB (2015) Expert systems with applications classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42:1106\u20131117. https:\/\/doi.org\/10.1016\/j.eswa.2014.08.030","journal-title":"Expert Syst Appl"},{"issue":"7","key":"11035_CR30","doi-asserted-by":"publisher","first-page":"1740003","DOI":"10.1142\/S0219519417400036","volume":"17","author":"M Sharma","year":"2017","unstructured":"Sharma M, Pachori RB (2017) A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. Journal of Mechanics in Medicine and Biology 17(7):1740003. https:\/\/doi.org\/10.1142\/S0219519417400036","journal-title":"Journal of Mechanics in Medicine and Biology"},{"key":"11035_CR31","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.patrec.2017.03.023","volume":"94","author":"M Sharma","year":"2017","unstructured":"Sharma M, Pachori RB, Rajendra Acharya U (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 94:172\u2013179. https:\/\/doi.org\/10.1016\/j.patrec.2017.03.023","journal-title":"Pattern Recogn Lett"},{"issue":"4","key":"11035_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2018.2882622","volume":"2","author":"RR Sharma","year":"2018","unstructured":"Sharma RR, Varshney P, Pachori RB, Vishvakarma SK (2018) Automated system for epileptic EEG detection using iterative filtering. IEEE Sensors Lett 2(4):1\u20134","journal-title":"IEEE Sensors Lett"},{"key":"11035_CR33","volume-title":"Epileptic seizure detection from EEG signals using best feature subsets based on estimation of mutual information for support vector machines and Na\u00efve Bayes classifiers, control and automation","author":"A Sharmila","year":"2016","unstructured":"A.Sharmila and P. Geethanjali (2016) Epileptic seizure detection from EEG signals using best feature subsets based on estimation of mutual information for support vector machines and Na\u00efve Bayes classifiers, control and automation"},{"issue":"0","key":"11035_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03091902.2017.1394389","volume":"0","author":"A Sharmila","year":"2018","unstructured":"Sharmila A, Suman AR, Pandey S, Mahalakshmi P (2018) Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine : a case study. J Med Eng Technol 0(0):1\u20138","journal-title":"J Med Eng Technol"},{"key":"11035_CR35","doi-asserted-by":"publisher","unstructured":"Siska B, Astuti F, Purnami SW, Atok RM, Islamiyah WR (2021) Classify epileptic EEG signals using extreme support vector machine for ictal and muscle artifact detection. 11(2). https:\/\/doi.org\/10.18178\/ijmlc.2021.11.2.1031","DOI":"10.18178\/ijmlc.2021.11.2.1031"},{"key":"11035_CR36","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.cmpb.2010.11.014","volume":"104","author":"S Siuly","year":"2010","unstructured":"Siuly S, Li Y (2010) Wen PP clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Prog Biomed 104:358\u2013372. https:\/\/doi.org\/10.1016\/j.cmpb.2010.11.014","journal-title":"Comput Methods Prog Biomed"},{"issue":"3","key":"11035_CR37","doi-asserted-by":"publisher","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 (2011) Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Prog Biomed 104(3):358\u2013372","journal-title":"Comput Methods Prog Biomed"},{"issue":"6","key":"11035_CR38","doi-asserted-by":"publisher","first-page":"556","DOI":"10.4236\/jbise.2010.36078","volume":"3","author":"Y Song","year":"2017","unstructured":"Song Y, Li\u00f2 P (2017) A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J Biomed Sci Eng 3(6):556\u2013567","journal-title":"J Biomed Sci Eng"},{"key":"11035_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s11571-016-9408-y","volume":"11","author":"SRN Sriraam","year":"2016","unstructured":"Sriraam SRN (2016) Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 11:51\u201366. https:\/\/doi.org\/10.1007\/s11571-016-9408-y","journal-title":"Cogn Neurodyn"},{"issue":"1","key":"11035_CR40","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s11571-016-9408-y","volume":"11","author":"SRN Sriraam","year":"2017","unstructured":"Sriraam SRN (2017) Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cognitive Neurodynamics 11(1):51\u201366","journal-title":"Cognitive Neurodynamics"},{"key":"11035_CR41","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.eswa.2016.02.040","volume":"56","author":"P Swami","year":"2016","unstructured":"Swami P, Gandhi TK, Panigrahi BK, Tripathi M, Anand S (2016) A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst Appl 56:116\u2013130","journal-title":"Expert Syst Appl"},{"issue":"4","key":"11035_CR42","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/JBHI.2016.2589971","volume":"21","author":"AK Tiwari","year":"2017","unstructured":"Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK (Jul 2017) Automated diagnosis of epilepsy using key-point based local binary pattern of EEG signals. IEEE J. Biomed. Health Inform 21(4):888\u2013896","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"11035_CR43","doi-asserted-by":"crossref","unstructured":"Tzallas A, Tsipouras M, Fotiadis Dware design of multiclass SVM classifi (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Computational Intelligence and Neuroscience","DOI":"10.1155\/2007\/80510"},{"key":"11035_CR44","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s12553-018-0265-z","volume":"9","author":"KD Tzimourta","year":"2019","unstructured":"Tzimourta KD, Tzallas AT, Giannakeas N, Astrakas LG, Tsalikakis DG, Angelidis P, Tsipouras MG (2019) A robust methodology for classification of epileptic seizures in EEG signals. Health Technol (Berl) 9:135\u2013142. https:\/\/doi.org\/10.1007\/s12553-018-0265-z","journal-title":"Health Technol (Berl)"},{"key":"11035_CR45","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.jneumeth.2005.06.005","volume":"150","author":"P Valenti","year":"2006","unstructured":"Valenti P et al (2006) Automatic detection of interictal spikes using data mining models. J Neurosci Methods 150:105\u2013110","journal-title":"J Neurosci Methods"},{"key":"11035_CR46","doi-asserted-by":"crossref","unstructured":"Wang Y, Li Z, Feng L, Bai H, Wang C (2017) Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection, pp. 108\u2013115","DOI":"10.1049\/iet-cds.2017.0216"},{"issue":"4","key":"11035_CR47","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/0013-4694(95)00277-4","volume":"98","author":"WRS Webber","year":"1996","unstructured":"Webber WRS, Lesser RP, Richardson RT, Wilson K (1996) An approach to seizure detection using an artificial neural network (ANN). Electroencephalogr Clin Neurophysiol 98(4):250\u2013272. https:\/\/doi.org\/10.1016\/0013-4694(95)00277-4","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"11035_CR48","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jneumeth.2015.01.015","volume":"243","author":"J Xiang","year":"2015","unstructured":"Xiang J, Li C, Li H, Cao R, Wang B, Han X, Chen J (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18\u201325","journal-title":"J Neurosci Methods"},{"key":"11035_CR49","doi-asserted-by":"publisher","first-page":"105472","DOI":"10.1016\/j.cmpb.2020.105472","volume":"193","author":"S You","year":"2020","unstructured":"You S, Cho BH, Yook S, Kim JY, Shon YM, Seo DW, Kim IY (2020) Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network. Comput Methods Prog Biomed 193:105472. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105472","journal-title":"Comput Methods Prog Biomed"},{"key":"11035_CR50","unstructured":"Yu J, Wang L, Chen X (2019) Epileptic seizure classification based on the combined features,0\u20135"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11035-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11035-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11035-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T08:16:33Z","timestamp":1653034593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11035-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,30]]},"references-count":50,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["11035"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11035-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,5,30]]},"assertion":[{"value":"14 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}