{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:00:20Z","timestamp":1773388820138,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06457-x","type":"journal-article","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T00:57:44Z","timestamp":1630717064000},"page":"1219-1237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A hybrid machine learning model for classifying time series"],"prefix":"10.1007","volume":"34","author":[{"given":"Abdullah","family":"Elen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emre","family":"Avu\u00e7lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"6457_CR1","first-page":"23","volume":"18","author":"S Ayd\u0131n","year":"2010","unstructured":"Ayd\u0131n S (2010) Determination of autoregressive model orders for seizure detection. Turkish J Electr Eng Comput Sci 18:23\u201330","journal-title":"Turkish J Electr Eng Comput Sci"},{"key":"6457_CR2","unstructured":"Kolb B, Whishaw IQ (1990) Fundamentals of Human Neuropsychology 3rd ed., Freeman WH and Company, New York, 697\u2013722"},{"key":"6457_CR3","unstructured":"Tzallas AT, Tsipouras MG, Tsalikakis DG, Karvounis EC, Astrakas L, Konitsiotis S, Tzaphlidou M (2012) Automated epileptic seizure detection methods: a review study, Epilepsy-histological, electroencephalographic, and psychological aspects, 75\u201398"},{"key":"6457_CR4","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/BF01797193","volume":"87","author":"H Berger","year":"1929","unstructured":"Berger H (1929) \u00dcber das elektroenkephalogramm des menschen. Arch Psychiatr Nervenkr 87:527\u2013570","journal-title":"Arch Psychiatr Nervenkr"},{"key":"6457_CR5","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1002\/9780470511923","volume-title":"EEG Signal Processing 1 ed.","author":"S Sanei","year":"2007","unstructured":"Sanei S, Chambers J (2007) EEG Signal Processing 1 ed. Wiley, England, pp 24\u201328"},{"key":"6457_CR6","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1006\/cbmr.1996.0022","volume":"29","author":"N Pradhan","year":"1996","unstructured":"Pradhan N, Sadasivan PK, Arunodaya GR (1996) Detection of seizure activity in EEG by an artificial neural network: a preliminary study. Comput Biomed Res 29:303\u2013313","journal-title":"Comput Biomed Res"},{"key":"6457_CR7","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/S0925-2312(99)00126-5","volume":"30","author":"A Petrosian","year":"2000","unstructured":"Petrosian A, Prokhorov D, Homan R, Dashei R, Wunsch D (2000) Recurrent neural network based prediction of epileptic seizures in intra and extracranial EEG. Neurocomputing 30:201\u2013218","journal-title":"Neurocomputing"},{"issue":"6","key":"6457_CR8","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10916-005-6133-1","volume":"29","author":"V Srinivasan","year":"2005","unstructured":"Srinivasan V, Eswaran C, Sriraam N (2005) Artificial neural network based epileptic detection using time domain and frequency domain features. J Med Syst 29(6):647\u2013660","journal-title":"J Med Syst"},{"issue":"1","key":"6457_CR9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1179\/016164104773026534","volume":"26","author":"V Nigam","year":"2004","unstructured":"Nigam V, Graupe D (2004) A neural-network-based detection of epilepsy. Neurol Res 26(1):55\u201360","journal-title":"Neurol Res"},{"key":"6457_CR10","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.cmpb.2004.10.009","volume":"78","author":"A Suba\u015f\u0131","year":"2005","unstructured":"Suba\u015f\u0131 A, Er\u00e7elebi E (2005) Classification of EEG signals using neural network and logistic regression. Comput Methods Prog Biomed 78:87\u201399","journal-title":"Comput Methods Prog Biomed"},{"key":"6457_CR11","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.eswa.2005.04.007","volume":"29","author":"A Suba\u015f\u0131","year":"2005","unstructured":"Suba\u015f\u0131 A (2005) Epileptic seizure detection using dynamic wavelet network. Expert Syst Appl 29:343\u2013355","journal-title":"Expert Syst Appl"},{"key":"6457_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.jneumeth.2005.04.009","volume":"148","author":"A Alkan","year":"2005","unstructured":"Alkan A, K\u00f6kl\u00fckaya E, Suba\u015f\u0131 A (2005) Automatic seizure detection in EEG using logistic regression and artificial neural network. J Neurosci Methods 148:167\u2013176","journal-title":"J Neurosci Methods"},{"key":"6457_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.cmpb.2005.06.012","volume":"80","author":"N Kannathal","year":"2005","unstructured":"Kannathal N, Choo ML, Acharyab UR, Sadasivana PK (2005) Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed 80:187\u2013194","journal-title":"Comput Methods Programs Biomed"},{"key":"6457_CR14","doi-asserted-by":"crossref","unstructured":"Fan J, Shao C, Yang O, Wang J, Li S, Wang Z (2006) Automatic seizure detection based on support vector machines with genetic algorithms, Simulated Evolution and Learning (SEAL), Hefei, 845\u2013852","DOI":"10.1007\/11903697_106"},{"issue":"3","key":"6457_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2007\/80510","volume":"7","author":"AT Tzallas","year":"2007","unstructured":"Tzallas AT, Tsipouras MG, Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci 7(3):1\u201313","journal-title":"Comput Intell Neurosci"},{"issue":"2","key":"6457_CR16","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":"6457_CR17","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1113\/jphysiol.1934.sp003147","volume":"81","author":"ED Adrian","year":"1934","unstructured":"Adrian ED, Matthews BHC (1934) The interpretation of potential waves in the cortex. J Physiol 81:440\u2013471","journal-title":"J Physiol"},{"key":"6457_CR18","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1152\/jn.1939.2.6.494","volume":"2","author":"PA Davis","year":"1939","unstructured":"Davis PA (1939) Effects of acoustic stimuli on the waking human brain. J Neurophysiol 2:494\u2013499","journal-title":"J Neurophysiol"},{"key":"6457_CR19","doi-asserted-by":"crossref","unstructured":"Hu J, Knapp B (1991) Electroencephalogram pattern recognition using fuzzy logic. In: IEEE Proceedings of the 25th Asilomar conference on signals, systems, and computers, Pacific Grove, CA, 2: 805\u2013807","DOI":"10.1109\/ACSSC.1991.186558"},{"key":"6457_CR20","unstructured":"Pacheco OR, Vaz F (1998) Integrated system for analysis and automatic classification of sleep EEG.In: Proceeding of the 20th annual international conference of the IEEE engineering in medicine and biology society, Hong Kong, 20(4): 2062\u20132065"},{"key":"6457_CR21","unstructured":"Oropesa E, Cycon HL, Jobert M (1999) Sleep stage classification using wavelet transform and neural network. In: International Computer Science Institute: Technical Reports, TR-99\u2013008: 1\u20137"},{"key":"6457_CR22","doi-asserted-by":"crossref","unstructured":"Flexer A, Sykacek P, Rezek I, Dorffner G (2000) Using hidden Markov models to build an automatic, continuous and probabilistic sleep stager. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks. IJCNN 2000. Neural computing: new challenges and perspectives for the New Millennium, vol. 3, pp. 627\u2013631","DOI":"10.1109\/IJCNN.2000.861392"},{"issue":"1","key":"6457_CR23","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s10527-007-0006-5","volume":"41","author":"L Doroshenkov","year":"2007","unstructured":"Doroshenkov L, Konyshev V, Selishchev S (2007) Classification of human sleep stages based on EEG processing using hidden Markov models. Biomed Eng 41(1):25\u201328","journal-title":"Biomed Eng"},{"key":"6457_CR24","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.1109\/10.966600","volume":"48","author":"R Agarwal","year":"2001","unstructured":"Agarwal R, Gotman J (2001) Computer-assisted sleep staging. IEEE Trans Biomed Eng 48:1412\u20131423","journal-title":"IEEE Trans Biomed Eng"},{"key":"6457_CR25","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/11011620_8","volume":"4","author":"G Becq","year":"2005","unstructured":"Becq G, Charbonnier S, Chapotot F, Buguet A, Bourdon L, Baconnier P (2005) Comparison between five classifiers for automatic scoring of human sleep recordings. Stud Comput Intell 4:113\u2013127","journal-title":"Stud Comput Intell"},{"key":"6457_CR26","doi-asserted-by":"publisher","first-page":"106071","DOI":"10.1016\/j.asoc.2020.106071","volume":"89","author":"M Liu","year":"2020","unstructured":"Liu M, Zhou M, Zhang T, Xiong N (2020) Semi-supervised learning quantization algorithm with deep features for motor imagery EEG Recognition in smart healthcare application. Appl Soft Comput 89:106071","journal-title":"Appl Soft Comput"},{"key":"6457_CR27","doi-asserted-by":"crossref","unstructured":"Yurui Ming, Weiping Ding, Danilo Pelusi, Dongrui Wu, Yu-Kai, Wang, Mukesh Prasad, & Chin-TengLin, \u201cSubject adaptation network for EEG data analysis\u201d, 84 (2019).","DOI":"10.1016\/j.asoc.2019.105689"},{"key":"6457_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106855","author":"A Elen","year":"2020","unstructured":"Elen A, Avu\u00e7lu E (2020) Standardized variable distances: a distance-based machine learning method. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2020.106855","journal-title":"Appl Soft Comput"},{"key":"6457_CR29","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1007\/s11517-020-02260-3","volume":"58","author":"E Avu\u00e7lu","year":"2020","unstructured":"Avu\u00e7lu E, Elen A (2020) Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements. Med Biol Eng Comput 58:2775\u20132788. https:\/\/doi.org\/10.1007\/s11517-020-02260-3","journal-title":"Med Biol Eng Comput"},{"issue":"1","key":"6457_CR30","first-page":"141","volume":"11","author":"A Elen","year":"2019","unstructured":"Elen A, Turan MK (2019) Classifying white blood cells using machine learning algorithms. Int J Eng Res Dev 11(1):141\u2013152","journal-title":"Int J Eng Res Dev"},{"issue":"2","key":"6457_CR31","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/S0169-2607(03)00079-8","volume":"74","author":"MG Tsipouras","year":"2004","unstructured":"Tsipouras MG, Fotiadis DI (2004) Automatic arrhythmia detection based on time and time frequency analysis of heart rate variability. Comp Meth Prog Biomed 74(2):95\u2013108","journal-title":"Comp Meth Prog Biomed"},{"key":"6457_CR32","doi-asserted-by":"crossref","unstructured":"Dliou A, Latif R, Laaboubi M, ve Maoulainine F (2014) Abnormal ECG signals analysis using non-parametric time-frequency techniques. Arab J Sci Eng 39(2)","DOI":"10.1007\/s13369-013-0687-x"},{"key":"6457_CR33","doi-asserted-by":"crossref","unstructured":"Gradl S, Kugler P, Lohm\u00fcller C, ve Eskofier B (2012) Real-time ECG monitoring and arrhythmia detection using android-based mobile devices, engineering in medicine and biology society (EMBC). In: 2012 Annual International Conference of the IEEE","DOI":"10.1109\/EMBC.2012.6346460"},{"key":"6457_CR34","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.protcy.2013.12.339","volume":"10","author":"M Mitra","year":"2013","unstructured":"Mitra M, ve Samanta, R. K, (2013) Cardiac arrhythmia classification using neural networks with selected features. Procedia Technol 10:76\u201384","journal-title":"Procedia Technol"},{"issue":"2","key":"6457_CR35","first-page":"233","volume":"14","author":"YC Yeh","year":"2012","unstructured":"Yeh YC (2012) An analysis of ECG for determining heartbeat case by using the principal component analysis and fuzzy logic. Int J Fuzzy Syst 14(2):233\u2013241","journal-title":"Int J Fuzzy Syst"},{"issue":"4","key":"6457_CR36","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1590\/2446-4740.05815","volume":"32","author":"RDF Dalvi","year":"2016","unstructured":"Dalvi RDF, Zago GT, ve Andre\u00e3o, R. V. (2016) Heartbeat classification system based on neural networks and dimensionality reduction. Res Biomed Eng 32(4):318\u2013326","journal-title":"Res Biomed Eng"},{"issue":"4","key":"6457_CR37","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1007\/s13534-014-0158-7","volume":"4","author":"Z Zhang","year":"2014","unstructured":"Zhang Z, ve Luo, X. (2014) Heartbeat classification using decision level fusion. Biomed Eng Lett 4(4):388\u2013395","journal-title":"Biomed Eng Lett"},{"key":"6457_CR38","doi-asserted-by":"crossref","unstructured":"Lin C-C, ve Chun-Min Y (2014) Heartbeat classification using normalized RR intervals and morphological features. Math Prob Eng","DOI":"10.1109\/IS3C.2014.175"},{"issue":"12","key":"6457_CR39","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.19026\/rjaset.9.2597","volume":"9","author":"K Muthuvel","year":"2015","unstructured":"Muthuvel K, ve Suresh, L. P. (2015) Hybrid features and classifier for classification of ECG signal. Res J Appl Sci Eng Technol 9(12):1034\u20131050","journal-title":"Res J Appl Sci Eng Technol"},{"issue":"2","key":"6457_CR40","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1088\/1361-6579\/aa5070","volume":"38","author":"CL Herry","year":"2017","unstructured":"Herry CL, Frasch M, Seely AJ, Wu HT (2017) Heart beat classification from single-lead ECG using the synchrosqueezing transform. Physiol Meas 38(2):171","journal-title":"Physiol Meas"},{"key":"6457_CR41","doi-asserted-by":"publisher","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak RG, Lehnertz K, Rieke C, Mormann F, 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 64:061907","journal-title":"Phys Rev E"},{"key":"6457_CR42","unstructured":"Dataset: ECG5000, http:\/\/www.timeseriesclassification.com\/description.php?Dataset=ECG5000, access date: 9.12.2020"},{"issue":"1","key":"6457_CR43","first-page":"36","volume":"5","author":"HB Kekre","year":"2011","unstructured":"Kekre HB, Athawale AA, Patki SA (2011) Steganalysis of LSB embedded images using gray level co-occurrence matrix. Int J Image Process (IJIP) 5(1):36\u201345","journal-title":"Int J Image Process (IJIP)"},{"issue":"2","key":"6457_CR44","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.jneumeth.2005.04.013","volume":"148","author":"I Guler","year":"2005","unstructured":"Guler I, Ubeyli ED (2005) Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods 148(2):113\u2013121","journal-title":"J Neurosci Methods"},{"key":"6457_CR45","doi-asserted-by":"crossref","unstructured":"Tzallas AT, Tsipouras MG, Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci 80510","DOI":"10.1155\/2007\/80510"},{"key":"6457_CR46","first-page":"3","volume":"2007","author":"AT Tzallas","year":"2007","unstructured":"Tzallas AT, Tsipouras MG, Fotiadis DI (2007) The use of time-frequency distributions for epileptic seizure detection in EEG recordings. Conf Proc IEEE Eng Med Biol Soc 2007:3\u20136","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"issue":"2","key":"6457_CR47","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TITB.2006.879600","volume":"11","author":"I Guler","year":"2007","unstructured":"Guler I, Ubeyli ED (2007) Multiclass support vector machines for EEG-signals classification. IEEE Trans Inf Technol Biomed 11(2):117\u2013126","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"6457_CR48","doi-asserted-by":"crossref","unstructured":"Liang SF, Wang HC, Chang WL (2010) Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J Adv Signal Process 853434","DOI":"10.1155\/2010\/853434"},{"key":"6457_CR49","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.neucom.2020.02.097","volume":"398","author":"J Azar","year":"2020","unstructured":"Azar J, Makhoul A, Couturier R, Demerjian J (2020) Robust IoT time series classification with data compression and deep learning. Neurocomputing 398:222\u2013234. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.097","journal-title":"Neurocomputing"},{"key":"6457_CR50","doi-asserted-by":"publisher","unstructured":"Du Q, Gu W, Zhang L, Huang S-L (2018) Attention-based LSTM-CNNs for time-series classification. In: Proceedings of the 16th ACM conference on embedded networked sensor systems - sensys'18, 410\u2013411. https:\/\/doi.org\/10.1145\/3274783.3275208","DOI":"10.1145\/3274783.3275208"},{"key":"6457_CR51","doi-asserted-by":"publisher","unstructured":"Junpeng BAO, Xinyi LI, Yu GAO (2019) MTCNN: a deep neural network for recognizing stochastic phase shifted time series data. Scientific conference on network, power systems and computing (NPSC 2019), 201\u2013206. https:\/\/doi.org\/10.33969\/EECS.V3.045","DOI":"10.33969\/EECS.V3.045"},{"issue":"5","key":"6457_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0197499","volume":"13","author":"Y Liu","year":"2018","unstructured":"Liu Y, Chen J, Wu S, Liu Z, Chao H (2018) Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance. PLoS ONE 13(5):1\u201325. https:\/\/doi.org\/10.1371\/journal.pone.0197499","journal-title":"PLoS ONE"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06457-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06457-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06457-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T15:59:30Z","timestamp":1673193570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06457-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,3]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6457"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06457-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,3]]},"assertion":[{"value":"2 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}