{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:47:16Z","timestamp":1774716436360,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T00:00:00Z","timestamp":1669507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T00:00:00Z","timestamp":1669507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00034-022-02223-z","type":"journal-article","created":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T11:35:27Z","timestamp":1669548927000},"page":"2782-2803","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Intelligent Method for Epilepsy Seizure Detection Based on Hybrid Nonlinear EEG Data Features Using Adaptive Signal Decomposition Methods"],"prefix":"10.1007","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2162-7673","authenticated-orcid":false,"given":"Sandeep","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harjot","family":"Kaur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,27]]},"reference":[{"issue":"4","key":"2223_CR1","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.bspc.2011.07.007","volume":"7","author":"UR Acharya","year":"2012","unstructured":"U.R. Acharya, F. Molinari, S.V. Sree, S. Chattopadhyay, K.H. Ng, J.S. Suri, Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4), 401\u2013408 (2012)","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"2223_CR2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s13246-020-00963-3","volume":"44","author":"H Akbari","year":"2021","unstructured":"H. Akbari, M.T. Sadiq, Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms. Phys. Eng. Sci. Med. 44(1), 157\u2013171 (2021)","journal-title":"Phys. Eng. Sci. Med."},{"key":"2223_CR3","doi-asserted-by":"crossref","unstructured":"M. Alolaiwy, M. Tanik, L. Jololian, From CNNs to adaptive filter design for digital image denoising using reinforcement q-learning. in SoutheastCon 2021 (IEEE, 2021), pp. 1\u20138","DOI":"10.1109\/SoutheastCon45413.2021.9401873"},{"issue":"1","key":"2223_CR4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s13246-015-0333-x","volume":"38","author":"HU Amin","year":"2015","unstructured":"H.U. Amin, A.S. Malik, R.F. Ahmad, N. Badruddin, N. Kamel, M. Hussain, W.T. Chooi, Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australas. Phys. Eng. Sci. Med. 38(1), 139\u2013149 (2015)","journal-title":"Australas. Phys. Eng. Sci. Med."},{"issue":"4","key":"2223_CR5","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3390\/app7040385","volume":"7","author":"A Bhattacharyya","year":"2017","unstructured":"A. Bhattacharyya, R.B. Pachori, A. Upadhyay, U.R. Acharya, Tunable-q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7(4), 385 (2017)","journal-title":"Appl. Sci."},{"key":"2223_CR6","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.dsp.2018.02.020","volume":"78","author":"A Bhattacharyya","year":"2018","unstructured":"A. Bhattacharyya, L. Singh, R.B. Pachori, Fourier\u2013Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals. Digit. Signal Process. 78, 185\u2013196 (2018)","journal-title":"Digit. Signal Process."},{"key":"2223_CR7","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Networks for Pattern Recognition","author":"CM Bishop","year":"1995","unstructured":"C.M. Bishop et al., Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1995)"},{"key":"2223_CR8","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102073","volume":"62","author":"VR Carvalho","year":"2020","unstructured":"V.R. Carvalho, M.F. Moraes, A.P. Braga, E.M. Mendes, Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification. Biomed. Signal Process. Control 62, 102073 (2020)","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"2223_CR9","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10479-006-0076-x","volume":"148","author":"WA Chaovalitwongse","year":"2006","unstructured":"W.A. Chaovalitwongse, O.A. Prokopyev, P.M. Pardalos, Electroencephalogram (EEG) time series classification: applications in epilepsy. Ann. Oper. Res. 148(1), 227\u2013250 (2006)","journal-title":"Ann. Oper. Res."},{"key":"2223_CR10","doi-asserted-by":"crossref","first-page":"61046","DOI":"10.1109\/ACCESS.2019.2915610","volume":"7","author":"S Chen","year":"2019","unstructured":"S. Chen, X. Zhang, L. Chen, Z. Yang, Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access 7, 61046\u201361056 (2019)","journal-title":"IEEE Access"},{"key":"2223_CR11","doi-asserted-by":"crossref","unstructured":"X. Chen, J.C. Jeong, Enhanced recursive feature elimination, in Sixth International Conference on Machine Learning and Applications (ICMLA 2007) (IEEE, 2007), pp. 429\u2013435","DOI":"10.1109\/ICMLA.2007.35"},{"key":"2223_CR12","doi-asserted-by":"crossref","unstructured":"M. Dalal, M. Tanveer, R.B. Pachori, Automated identification system for focal EEG signals using fractal dimension of FAWT-based sub-bands signals, in Machine Intelligence and Signal Analysis (Springer, 2019), pp. 583\u2013596","DOI":"10.1007\/978-981-13-0923-6_50"},{"key":"2223_CR13","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.bspc.2016.05.004","volume":"29","author":"AB Das","year":"2016","unstructured":"A.B. Das, M.I.H. Bhuiyan, Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed. Signal Process. Control 29, 11\u201321 (2016)","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"2223_CR14","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2013","unstructured":"K. Dragomiretskiy, D. Zosso, Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531\u2013544 (2013)","journal-title":"IEEE Trans. Signal Process."},{"key":"2223_CR15","unstructured":"R. Esteller, J. Echauz, T. Tcheng, B. Litt, B. Pless Line length: an efficient feature for seizure onset detection, in 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol\u00a02 (IEEE, 2001), pp. 1707\u20131710"},{"key":"2223_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neulet.2018.10.062","volume":"694","author":"O Fasil","year":"2019","unstructured":"O. Fasil, R. Rajesh, Time-domain exponential energy for epileptic EEG signal classification. Neurosci. Lett. 694, 1\u20138 (2019)","journal-title":"Neurosci. Lett."},{"key":"2223_CR17","doi-asserted-by":"crossref","first-page":"24301","DOI":"10.1109\/ACCESS.2017.2766232","volume":"5","author":"Z Feng","year":"2017","unstructured":"Z. Feng, D. Zhang, M.J. Zuo, Adaptive mode decomposition methods and their applications in signal analysis for machinery fault diagnosis: a review with examples. IEEE Access 5, 24301\u201324331 (2017)","journal-title":"IEEE Access"},{"issue":"2","key":"2223_CR18","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/LSP.2003.821662","volume":"11","author":"P Flandrin","year":"2004","unstructured":"P. Flandrin, G. Rilling, P. Goncalves, Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112\u2013114 (2004)","journal-title":"IEEE Signal Process. Lett."},{"issue":"1","key":"2223_CR19","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"M. Friedman, A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86\u201392 (1940)","journal-title":"Ann. Math. Stat."},{"key":"2223_CR20","doi-asserted-by":"crossref","DOI":"10.4324\/9780429056765","volume-title":"IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference","author":"D George","year":"2019","unstructured":"D. George, P. Mallery, IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference (Routledge, London, 2019)"},{"issue":"1","key":"2223_CR21","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1137\/130923774","volume":"7","author":"J Gilles","year":"2014","unstructured":"J. Gilles, G. Tran, S. Osher, 2D empirical transforms. Wavelets, ridgelets, and curvelets revisited. SIAM J. Imaging Sci. 7(1), 157\u2013186 (2014)","journal-title":"SIAM J. Imaging Sci."},{"issue":"1","key":"2223_CR22","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jneumeth.2010.05.020","volume":"191","author":"L Guo","year":"2010","unstructured":"L. Guo, D. Rivero, J. Dorado, J.R. Rabunal, A. Pazos, Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods 191(1), 101\u2013109 (2010)","journal-title":"J. Neurosci. Methods"},{"issue":"6","key":"2223_CR23","doi-asserted-by":"crossref","first-page":"479","DOI":"10.29252\/nirp.bcn.8.6.479","volume":"8","author":"SA Hosseini","year":"2017","unstructured":"S.A. Hosseini, A hybrid approach based on higher order spectra for clinical recognition of seizure and epilepsy using brain activity. Basic Clin. Neurosci. 8(6), 479 (2017)","journal-title":"Basic Clin. Neurosci."},{"issue":"10","key":"2223_CR24","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1016\/j.clinph.2014.02.015","volume":"125","author":"N Koolen","year":"2014","unstructured":"N. Koolen, K. Jansen, J. Vervisch, V. Matic, M. De Vos, G. Naulaers, S. Van Huffel, Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings. Clin. Neurophysiol. 125(10), 1985\u20131994 (2014)","journal-title":"Clin. Neurophysiol."},{"issue":"11","key":"2223_CR25","first-page":"11","volume":"2","author":"G Kumar","year":"2014","unstructured":"G. Kumar, Evaluation metrics for intrusion detection systems\u2014a study. Evaluation 2(11), 11\u20137 (2014)","journal-title":"Evaluation"},{"issue":"6","key":"2223_CR26","doi-asserted-by":"crossref","first-page":"13521","DOI":"10.1007\/s10586-018-1995-4","volume":"22","author":"MR Kumar","year":"2019","unstructured":"M.R. Kumar, Y.S. Rao, Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition. Clust. Comput. 22(6), 13521\u201313531 (2019)","journal-title":"Clust. Comput."},{"issue":"6","key":"2223_CR27","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/03772063.2014.963173","volume":"60","author":"R Kumar","year":"2014","unstructured":"R. Kumar, I. Saini, Empirical wavelet transform based ECG signal compression. IETE J. Res. 60(6), 423\u2013431 (2014)","journal-title":"IETE J. Res."},{"issue":"7","key":"2223_CR28","first-page":"6","volume":"2","author":"Y Kumar","year":"2011","unstructured":"Y. Kumar, M. Dewal, Complexity measures for normal and epileptic EEG signals using ApEn, SampEn and SEN. IJCCT 2(7), 6\u201312 (2011)","journal-title":"IJCCT"},{"issue":"1","key":"2223_CR29","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/LGRS.2015.2493198","volume":"13","author":"W Liu","year":"2015","unstructured":"W. Liu, S. Cao, Y. Chen, Seismic time\u2013frequency analysis via empirical wavelet transform. IEEE Geosci. Remote Sens. Lett. 13(1), 28\u201332 (2015)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"2223_CR30","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3389\/fnsys.2018.00043","volume":"12","author":"F Manzouri","year":"2018","unstructured":"F. Manzouri, S. Heller, M. D\u00fcmpelmann, P. Woias, A. Schulze-Bonhage, A comparison of machine learning classifiers for energy-efficient implementation of seizure detection. Front. Syst. Neurosci. 12, 43 (2018)","journal-title":"Front. Syst. Neurosci."},{"issue":"2","key":"2223_CR31","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1016\/j.eswa.2007.12.065","volume":"36","author":"H Ocak","year":"2009","unstructured":"H. Ocak, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36(2), 2027\u20132036 (2009)","journal-title":"Expert Syst. Appl."},{"key":"2223_CR32","doi-asserted-by":"crossref","unstructured":"P.R. Pal, R. Panda, Classification of EEG signals for epileptic seizure evaluation, in 2010 IEEE Students Technology Symposium (TechSym) (IEEE, 2010), pp. 72\u201376","DOI":"10.1109\/TECHSYM.2010.5469195"},{"key":"2223_CR33","volume":"132","author":"H Peng","year":"2021","unstructured":"H. Peng, C. Lei, S. Zheng, C. Zhao, C. Wu, J. Sun, B. Hu, Automatic epileptic seizure detection via stein kernel-based sparse representation. Comput. Biol. Med. 132, 104338 (2021)","journal-title":"Comput. Biol. Med."},{"key":"2223_CR34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.eswa.2018.06.031","volume":"113","author":"S Raghu","year":"2018","unstructured":"S. Raghu, N. Sriraam, Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms. Expert Syst. Appl. 113, 18\u201332 (2018)","journal-title":"Expert Syst. Appl."},{"key":"2223_CR35","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.bspc.2019.01.012","volume":"50","author":"MM Rahman","year":"2019","unstructured":"M.M. Rahman, M.I.H. Bhuiyan, A.B. Das, Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking. Biomed. Signal Process. Control 50, 72\u201382 (2019)","journal-title":"Biomed. Signal Process. Control"},{"key":"2223_CR36","volume-title":"EEG Signal Processing","author":"S Sanei","year":"2013","unstructured":"S. Sanei, J.A. Chambers, EEG Signal Processing (John Wiley & Sons, Hoboken, NJ, 2013)"},{"issue":"5","key":"2223_CR37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1234-4","volume":"43","author":"RS Selvakumari","year":"2019","unstructured":"R.S. Selvakumari, M. Mahalakshmi, P. Prashalee, Patient-specific seizure detection method using hybrid classifier with optimized electrodes. J. Med. Syst. 43(5), 1\u20137 (2019)","journal-title":"J. Med. Syst."},{"key":"2223_CR38","volume-title":"Epilepsy Statistics","author":"PO Shafer","year":"2014","unstructured":"P.O. Shafer, J.I. Sirven, Epilepsy Statistics (Epilepsy Foundation, Bowie, 2014)"},{"key":"2223_CR39","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.knosys.2016.11.024","volume":"118","author":"M Sharma","year":"2017","unstructured":"M. Sharma, A. Dhere, R.B. Pachori, U.R. Acharya, An automatic detection of focal EEG signals using new class of time\u2013frequency localized orthogonal wavelet filter banks. Knowl.-Based Syst. 118, 217\u2013227 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"2223_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-020-00105-1","volume":"7","author":"MK Siddiqui","year":"2020","unstructured":"M.K. Siddiqui, R. Morales-Menendez, X. Huang, N. Hussain, A review of epileptic seizure detection using machine learning classifiers. Brain Inform. 7, 1\u201318 (2020)","journal-title":"Brain Inform."},{"issue":"07","key":"2223_CR41","doi-asserted-by":"crossref","first-page":"1740002","DOI":"10.1142\/S0219519417400024","volume":"17","author":"P Singh","year":"2017","unstructured":"P. Singh, R.B. Pachori, Classification of focal and nonfocal EEG signals using features derived from Fourier-based rhythms. J. Mech. Med. Biol. 17(07), 1740002 (2017)","journal-title":"J. Mech. Med. Biol."},{"issue":"06","key":"2223_CR42","doi-asserted-by":"crossref","first-page":"556","DOI":"10.4236\/jbise.2010.36078","volume":"3","author":"Y Song","year":"2010","unstructured":"Y. Song, P. Li\u00f2 et al., A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J. Biomed. Sci. Eng. 3(06), 556 (2010)","journal-title":"J. Biomed. Sci. Eng."},{"key":"2223_CR43","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.eswa.2016.02.040","volume":"56","author":"P Swami","year":"2016","unstructured":"P. Swami, T.K. Gandhi, B.K. Panigrahi, M. Tripathi, S. Anand, A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst. Appl. 56, 116\u2013130 (2016)","journal-title":"Expert Syst. Appl."},{"key":"2223_CR44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3389\/fnhum.2019.00052","volume":"13","author":"X Wang","year":"2019","unstructured":"X. Wang, G. Gong, N. Li, S. Qiu, Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization. Front. Hum. Neurosci. 13, 52 (2019)","journal-title":"Front. Hum. Neurosci."},{"key":"2223_CR45","unstructured":"WHO Epilepsy. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/epilepsy(2021)"},{"key":"2223_CR46","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"F. Wilcoxon, Individual comparisons by ranking methods. Biometrics 1, 80\u201383 (1945)","journal-title":"Biometrics"},{"issue":"5","key":"2223_CR47","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s00521-014-1786-7","volume":"26","author":"Y Zhang","year":"2015","unstructured":"Y. Zhang, Y. Zhang, J. Wang, X. Zheng, Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput. Appl. 26(5), 1217\u20131225 (2015)","journal-title":"Neural Comput. Appl."}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-022-02223-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-022-02223-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-022-02223-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T15:56:47Z","timestamp":1728489407000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-022-02223-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,27]]},"references-count":47,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["2223"],"URL":"https:\/\/doi.org\/10.1007\/s00034-022-02223-z","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,27]]},"assertion":[{"value":"12 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or nonfinancial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This is an observational study, and the Neurology and Sleep Centre-New Delhi has confirmed that no ethical approval is required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}}]}}