{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:43:10Z","timestamp":1774586590114,"version":"3.50.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2014,3,1]],"date-time":"2014-03-01T00:00:00Z","timestamp":1393632000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"published-print":{"date-parts":[[2014,3]]},"DOI":"10.1007\/s10916-014-0018-0","type":"journal-article","created":{"date-parts":[[2014,3,8]],"date-time":"2014-03-08T01:51:27Z","timestamp":1394243487000},"source":"Crossref","is-referenced-by-count":210,"title":["A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms"],"prefix":"10.1007","volume":"38","author":[{"given":"Baha","family":"\u015een","sequence":"first","affiliation":[]},{"given":"Musa","family":"Peker","sequence":"additional","affiliation":[]},{"given":"Abdullah","family":"\u00c7avu\u015fo\u011flu","sequence":"additional","affiliation":[]},{"given":"Fatih V.","family":"\u00c7elebi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,3,9]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/1475-925X-11-52","volume":"11","author":"ST Pan","year":"2012","unstructured":"Pan, S. T., Kuo, C. E., Zeng, J. H., and Liang, S. F., A transition-constrained discrete hidden Markov model for automatic sleep staging. BioMedical Eng OnLine. 11:52\u201371, 2012.","journal-title":"BioMedical Eng OnLine."},{"key":"18_CR2","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.3906\/elk-1203-9","volume":"21","author":"B Sen","year":"2013","unstructured":"Sen, B., and Peker, M., Novel approaches for automated epileptic diagnosis using FCBF feature selection and classification algorithms. Turk. J. Electr. Eng. Comput. Sci. 21:2092\u20132109, 2013.","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"issue":"1","key":"18_CR3","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cmpb.2011.11.005","volume":"108","author":"L Fraiwan","year":"2012","unstructured":"Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., and Dickhaus, H., Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Methods Prog Biomed 108(1):10\u201319, 2012.","journal-title":"Comput Methods Prog Biomed"},{"key":"18_CR4","first-page":"102","volume":"7","author":"RB Artan","year":"2008","unstructured":"Artan, R. B., and Yazgan, E., Epileptic seizure detection from SEEG data by using higher order statistics and spectra. it\u00fcdergisi 7:102\u2013111, 2008.","journal-title":"it\u00fcdergisi"},{"issue":"1","key":"18_CR5","first-page":"108","volume":"2","author":"T Fathima","year":"2010","unstructured":"Fathima, T., Bedeeuzzaman, M., Farooq, O., and Khan, Y. U., Wavelet based features for epileptic seizure detection. MES J of Technol and Manag. 2(1):108\u2013112, 2010.","journal-title":"MES J of Technol and Manag."},{"key":"18_CR6","first-page":"71","volume":"1","author":"CT Yuen","year":"2009","unstructured":"Yuen, C. T., San, W. S., Rizoni, M., and Seong, T. C., Classification of human emotions from EEG signals using statistical features and neural network. Int. J Integr Eng. 1:71\u201379, 2009.","journal-title":"Int. J Integr Eng."},{"issue":"1","key":"18_CR7","first-page":"1","volume":"4","author":"M Albayrak","year":"2009","unstructured":"Albayrak, M., and Koklukaya, E., The detection of an epileptiform activity on EEG signals by using data mining process. e-Journal of New World Sci. Acad 4(1):1\u201312, 2009.","journal-title":"e-Journal of New World Sci. Acad"},{"key":"18_CR8","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1016\/j.eswa.2006.02.005","volume":"32","author":"A Subasi","year":"2007","unstructured":"Subasi, A., EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32:1084\u20131093, 2007.","journal-title":"Expert Syst Appl"},{"key":"18_CR9","author":"S Ozsen","year":"2012","unstructured":"Ozsen, S., Classification of sleep stages using class-dependent sequential feature selection and artificial neural network. Neural Comput. & Applic. 2012. doi: 10.1007\/s00521-012-1065-4 .","journal-title":"Neural Comput. & Applic."},{"issue":"4","key":"18_CR10","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.1016\/j.eswa.2011.09.093","volume":"39","author":"TK Gandhi","year":"2012","unstructured":"Gandhi, T. K., Chakraborty, P., Roy, G. G., and Panigrahi, B. K., Discrete harmony search based expert model for epileptic seizure detection in electroencephalography. Expert Syst Appl 39(4):4055\u20134062, 2012.","journal-title":"Expert Syst Appl"},{"key":"18_CR11","unstructured":"Mohseni, H. R., Maghsoudi, A., and Shamsollahi, M. B., Seizure detection in EEG signals: A comparison of different approaches. IEEE EMBS\u201906. pp. 6724\u20136727, 2006."},{"key":"18_CR12","unstructured":"Alessandro, M. D\u2019, Vachtsevanos, G., Hinson, A., Esteller, R., Echauz, J., and Litt, B., A genetic approach to selecting the optimal feature for epileptic seizure prediction. IEEE EMBC\u201901, pp. 1703\u20131706, 2001."},{"key":"18_CR13","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.cmpb.2005.06.012","volume":"80","author":"N Kannathal","year":"2005","unstructured":"Kannathal, N., Choo, M., Acharya, U., and Sadasivan, P., Entropies for detection of epilepsy in EEG. Comput Methods Prog Biomed 80:187\u2013194, 2005.","journal-title":"Comput Methods Prog Biomed"},{"key":"18_CR14","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10916-005-6133-1","volume":"29","author":"V Srinivasan","year":"2005","unstructured":"Srinivasan, V., Eswaran, C., and Sriraam, N., Artificial neural network based epileptic detection using time domain and frequency domain features. J Med Syst 29:647\u2013660, 2005.","journal-title":"J Med Syst"},{"issue":"1","key":"18_CR15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10072-008-0851-3","volume":"29","author":"AA Bruzzo","year":"2008","unstructured":"Bruzzo, A. A., Gesierich, B., Santi, M., Tassinari, C. A., Birbaumer, N., and Rubboli, G., Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients-A preliminary study. Neurol Sci 29(1):3\u20139, 2008.","journal-title":"Neurol Sci"},{"issue":"9","key":"18_CR16","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1179\/1743132811Y.0000000041","volume":"33","author":"S Geng","year":"2011","unstructured":"Geng, S., Zhou, W., Yuan, Q., Cai, D., and Zeng, Y., EEG non-linear feature extraction using correlation dimension and Hurst exponent. Neurol Res 33(9):908\u2013912, 2011.","journal-title":"Neurol Res"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Bao, F. S., Lie, D. Y., and Zhang, Y., A new approach to automated epileptic diagnosis using EEG and probabilistic neural network. ICTAI\u201908. pp. 482\u2013486, 2008.","DOI":"10.1109\/ICTAI.2008.99"},{"issue":"1","key":"18_CR18","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s10916-010-9480-5","volume":"36","author":"E Sezer","year":"2012","unstructured":"Sezer, E., Isik, H., and Saracoglu, E., Employment and comparison of different Artificial Neural Networks for epilepsy diagnosis from EEG signals. J Med Syst 36(1):347\u201362, 2012.","journal-title":"J Med Syst"},{"key":"18_CR19","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1007\/BF02513332","volume":"37","author":"CA Holzmann","year":"1999","unstructured":"Holzmann, C. A., Pe\u00b4rez, C. A., Held, C. M., Mart\u0131\u00b4n, M. S., Pizarro, F., Pe\u00b4rez, J. P., Garrido, M., and Pierano, P., Expert-system classification of sleep\/waking states in infants. Med Biological Biol. Eng. Comput. 37:466\u2013476, 1999.","journal-title":"Med Biological Biol. Eng. Comput."},{"key":"18_CR20","unstructured":"Oropesa, E., Cycon, H. L., and Jobert, M., Sleep stage classification using wavelet transform and neural network. ICSI Technical Report TR-99-008. pp. 1\u20137, 1999."},{"key":"18_CR21","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1109\/10.966600","volume":"48","author":"R Agarwal","year":"2001","unstructured":"Agarwal, R., and Gotman, J., Computer-assisted sleep staging. IEEE Trans Biomed Eng 48:1412\u20131423, 2001.","journal-title":"IEEE Trans Biomed Eng"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Estrada, E., Nazeran, H., Nava, P., Behmehani, K., Burk, J., and Lucas, E., EEG feature extraction for classification of sleep stages. In: Proceedings of the 26th annual conference of the IEEE EMBS. San Francisco. pp. 196\u2013199, 2004.","DOI":"10.1109\/IEMBS.2004.1403125"},{"key":"18_CR23","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., and Baconnier, P., Comparison between five classifiers for automatic scoring of human sleep recordings. Stud Comput Intell. 4:113\u2013127, 2005.","journal-title":"Stud Comput Intell."},{"key":"18_CR24","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10916-008-9134-z","volume":"32","author":"RK Sinha","year":"2008","unstructured":"Sinha, R. K., Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. J Med Syst 32:291\u2013299, 2008.","journal-title":"J Med Syst"},{"key":"18_CR25","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.artmed.2008.07.005","volume":"44","author":"K \u0160u\u0161m\u00e1kov\u00e1","year":"2008","unstructured":"\u0160u\u0161m\u00e1kov\u00e1, K., and Krakovsk\u00e1, A., Discrimination ability of individual measures used in sleep stages classification. Artif Intell Med 44:261\u2013277, 2008.","journal-title":"Artif Intell Med"},{"key":"18_CR26","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1002\/acs.1147","volume":"24","author":"F Chapotot","year":"2010","unstructured":"Chapotot, F., and Becq, G., Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules. Int J Adapt Control and Signal Process 24:409\u2013423, 2010.","journal-title":"Int J Adapt Control and Signal Process"},{"issue":"1","key":"18_CR27","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s00521-004-0441-0","volume":"14","author":"A Subasi","year":"2005","unstructured":"Subasi, A., Kiymik, M. K., Akin, M., and Erogul, O., Automatic recognition of vigilance state by using wavelet-based artificial neural network. Neural Comput Appl.. 14(1):45\u201355, 2005.","journal-title":"Neural Comput Appl.."},{"key":"18_CR28","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.bspc.2007.05.005","volume":"2","author":"L Zoubek","year":"2007","unstructured":"Zoubek, L., Charbonnier, S., Lesecq, S., Buguet, A., and Chapotot, F., Feature selection for sleep\/wake stages classification using data driven methods. Biomed Signal Process Control. 2:171\u2013179, 2007.","journal-title":"Biomed Signal Process Control."},{"key":"18_CR29","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10527-007-0006-5","volume":"41","author":"LG Doroshenkov","year":"2007","unstructured":"Doroshenkov, L. G., Konyshev, V. A., and Selishchev, S. V., Classification of human sleep stages based on EEG processing using hidden markov models. Biomed Eng 41:25\u201328, 2007.","journal-title":"Biomed Eng"},{"key":"18_CR30","unstructured":"Ebrahimi, F., Mikaeili, M., Estrada, E., and Nazeran, H., Automatic sleep stage classification based on EEG signals using neural networks and wavelet packet coefficients. Proceeding of IEEE EMBC. pp. 1151\u20131154, 2008."},{"key":"18_CR31","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.compbiomed.2010.04.007","volume":"40","author":"HG Jo","year":"2010","unstructured":"Jo, H. G., Park, J. Y., Lee, C. K., An, S. K., and Yoo, S. K., Genetic fuzzy classifier for sleep stage identification. Comput Biol Med 40:629\u2013634, 2010.","journal-title":"Comput Biol Med"},{"key":"18_CR32","unstructured":"Gunes, S., Polat, K., Yosunkaya, S., and Dursun, M., A novel data pre-processing method on automatic determining of sleep stages: K-means clustering based feature weighting. Complex Systems and Applications-ICCSA. Le Havre-France. pp. 112\u2013117, 2009."},{"key":"18_CR33","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s10916-009-9286-5","volume":"34","author":"ME Tagluk","year":"2010","unstructured":"Tagluk, M. E., Sezgin, N., and Akin, M., Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J Med Syst 34:717\u2013725, 2010.","journal-title":"J Med Syst"},{"issue":"3","key":"18_CR34","doi-asserted-by":"crossref","first-page":"230","DOI":"10.3414\/ME09-01-0054","volume":"49","author":"L Fraiwan","year":"2010","unstructured":"Fraiwan, L., Lweesy, K., Khasawneh, N., Fraiwan, M., Wenz, H., and Dickhaus, H., Classification of sleep stages using multi-wavelet time frequency entropy and LDA. Methods Inf Med 49(3):230\u2013237, 2010.","journal-title":"Methods Inf Med"},{"key":"18_CR35","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neucom.2012.11.003","volume":"104","author":"YL Hsu","year":"2013","unstructured":"Hsu, Y. L., Yang, Y. T., Wang, J. S., and Hsu, C. Y., Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 104:105\u2013114, 2013.","journal-title":"Neurocomputing"},{"issue":"23","key":"18_CR36","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A. L., Amaral, L. A., Glass, L., et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):215\u2013220, 2000.","journal-title":"Circulation"},{"issue":"4","key":"18_CR37","first-page":"435","volume":"1","author":"JR Smith","year":"1979","unstructured":"Smith, J. R., Karacan, I., and Yang, M., Automated EEG analysis with microcomputers. Sleep 1(4):435\u2013443, 1979.","journal-title":"Sleep"},{"key":"18_CR38","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1097\/00001756-199907130-00028","volume":"10","author":"MLV Quyen","year":"1999","unstructured":"Quyen, M. L. V., Martinerie, J., Baulac, M., and Varela, F., Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. Neuroreport 10:2149\u2013215, 1999.","journal-title":"Neuroreport"},{"key":"18_CR39","unstructured":"Hjorth, B., Time domain descriptors and their relation to a particular model for generation of EEG activity. In: CEAN \u2013 Computerized EEG analysis, Stuttgart: Gustav Fischer Verlag. pp. 3\u20138, 1975."},{"key":"18_CR40","doi-asserted-by":"crossref","unstructured":"Petrosian, A., Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns. IEEE CBMS\u2019 95. pp. 212\u2013217, 1995.","DOI":"10.1109\/CBMS.1995.465426"},{"key":"18_CR41","first-page":"1025","volume":"7","author":"AB Gardner","year":"2006","unstructured":"Gardner, A. B., Krieger, A. E., Vachtsevanos, G., and Litt, B., One-class novelty detection for seizure analysis from intracranial EEG. J Mach Learn Res 7:1025\u20131044, 2006.","journal-title":"J Mach Learn Res"},{"key":"18_CR42","unstructured":"Esteller, R., Echaus, J., Tcheng, T., Litt, B., and Pless, B., Line length: an efficient feature for seizure onset detection. IEEE EMBS\u201901. pp. 1707\u20131710, 2001."},{"key":"18_CR43","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0010-4825(88)90041-8","volume":"18","author":"MJ Katz","year":"1988","unstructured":"Katz, M. J., Fractals and the analysis of waveforms. Comput Biol Med 18:145\u2013156, 1988.","journal-title":"Comput Biol Med"},{"key":"18_CR44","unstructured":"Avsar, E., Epileptic EEG signal classification using one-class support vector machines, Istanbul Technical University. M.Sc. Thesis. 2009."},{"key":"18_CR45","unstructured":"Hasiloglu, A., Rotation-Invariant texture analysis and classification by artificial neural networks and wavelet transform. Technical report, 1999."},{"issue":"2","key":"18_CR46","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.compbiomed.2005.12.003","volume":"37","author":"A Subasi","year":"2007","unstructured":"Subasi, A., Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Comput Biol Med 37(2):227\u2013244, 2007.","journal-title":"Comput Biol Med"},{"issue":"1","key":"18_CR47","first-page":"1","volume":"1","author":"K Mahajan","year":"2011","unstructured":"Mahajan, K., Vargantwar, M. R., and Rajput, M. S., Classification of EEG using PCA, ICA and Neural Network. Int. J. Eng Adv. Technol. (IJEAT) 1(1):1\u20135, 2011.","journal-title":"Int. J. Eng Adv. Technol. (IJEAT)"},{"key":"18_CR48","first-page":"1121","volume":"3","author":"M Peker","year":"2013","unstructured":"Peker, M., and Sen, B., A new complex-valued intelligent system for automated epilepsy diagnosis using EEG signals. Glob J Technol: 3rd World Conference on Inf Technol. 3:1121\u20131128, 2013.","journal-title":"Glob J Technol: 3rd World Conference on Inf Technol."},{"key":"18_CR49","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.artmed.2009.03.003","volume":"47","author":"M Sabeti","year":"2009","unstructured":"Sabeti, M., Katebi, S., and Boostani, R., Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med 47:263\u2013274, 2009.","journal-title":"Artif Intell Med"},{"key":"18_CR50","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/BF02024393","volume":"6","author":"A R\u00e9nyi","year":"1995","unstructured":"R\u00e9nyi, A., On a new axiomatic theory of probability. Acta Math Hung. 6:285\u2013335, 1995.","journal-title":"Acta Math Hung."},{"key":"18_CR51","unstructured":"Approximate entropy, http:\/\/en.wikipedia.org\/wiki\/Approximate_entropy (Accessed: 10.10.2012)"},{"issue":"3","key":"18_CR52","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10916-008-9245-6","volume":"34","author":"L Xu","year":"2010","unstructured":"Xu, L., Meng, M. Q. H., Qi, X., and Wang, K., Morphology variability analysis of wrist pulse waveform for assessment of arteriosclerosis status. J Med Syst 34(3):331\u2013339, 2010.","journal-title":"J Med Syst"},{"key":"18_CR53","doi-asserted-by":"crossref","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., and Cai, D., Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96:29\u201338, 2011.","journal-title":"Epilepsy Res"},{"key":"18_CR54","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","volume":"44","author":"UR Acharya","year":"2006","unstructured":"Acharya, U. R., Joseph, K. P., Kannathal, N., Lim, C. M., and Suri, J. S., Heart rate variability: a review. Med Biol Eng Comput 44:1031\u20131051, 2006.","journal-title":"Med Biol Eng Comput"},{"key":"18_CR55","first-page":"1643","volume":"266","author":"SM Pincus","year":"1994","unstructured":"Pincus, S. M., and Goldberger, A. L., Physiological time-series analysis: what does regularity quantify? Am Physiol. Soc.. 266:1643\u20131656, 1994.","journal-title":"Am Physiol. Soc.."},{"issue":"17","key":"18_CR56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1103\/PhysRevLett.88.174102","volume":"88","author":"C Bandt","year":"2002","unstructured":"Bandt, C., and Pompe, B., Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):1\u20134, 2002.","journal-title":"Phys Rev Lett"},{"key":"18_CR57","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1088\/1674-1056\/18\/7\/011","volume":"18","author":"XF Liu","year":"2009","unstructured":"Liu, X. F., and Wang, Y., Fine-grained permutation entropy as a measure of natural complexity for time series. Chinese Phys B 18:2690\u20132695, 2009.","journal-title":"Chinese Phys B"},{"key":"18_CR58","doi-asserted-by":"crossref","unstructured":"Cao, B., Shen, D., Sun, J. T., Yang, Q., and Chen, Z., Feature selection in a kernel Space. 24th Annual International Conference on Machine Learning, pp. 121\u2013128, 2007.","DOI":"10.1145\/1273496.1273512"},{"key":"18_CR59","unstructured":"Yu, L., and Liu, H., Feature selection for high-dimensional data: A fast correlation-based filter solution. ICML\u201903. pp. 856\u2013863, 2003."},{"key":"18_CR60","doi-asserted-by":"crossref","unstructured":"Ding, C., and Peng, H. C., Minimum redundancy feature selection from microarray gene expression data, Second IEEE Computational Systems Bioinformatics Conference. pp. 523\u2013528, 2003.","DOI":"10.1109\/CSB.2003.1227396"},{"key":"18_CR61","doi-asserted-by":"crossref","unstructured":"Kononenko, I., Estimating attributes: Analysis and extensions of RELIEF. ECML\u201994. pp. 171\u2013182, 1994.","DOI":"10.1007\/3-540-57868-4_57"},{"issue":"10","key":"18_CR62","doi-asserted-by":"crossref","first-page":"9468","DOI":"10.1016\/j.eswa.2012.02.112","volume":"39","author":"B Sen","year":"2012","unstructured":"Sen, B., Ucar, E., and Delen, D., Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Syst Appl 39(10):9468\u20139476, 2012.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"18_CR63","first-page":"36","volume":"2","author":"T Kavzoglu","year":"2010","unstructured":"Kavzoglu, T., and Colkesen, I., Classification of satellite images using decision trees: Kocaeli case. Electron. J Map Technol. 2(1):36\u201345, 2010.","journal-title":"Electron. J Map Technol."},{"key":"18_CR64","volume-title":"C4.5: Programs for machine learning","author":"L Quinlan","year":"1993","unstructured":"Quinlan, L., C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, 1993."},{"key":"18_CR65","unstructured":"Akgobek, O., Application of inductive learning to gain knowledge of an expert system. VI. Production Research Symposium. pp. 1\u20134, 2006."},{"issue":"9","key":"18_CR66","first-page":"1026","volume":"5","author":"S Yigit","year":"2011","unstructured":"Yigit, S., Eryigit, R., and Celebi, F. V., Optical gain model proposed with the use of artificial neural networks optimised by artificial bee colony algorithm. Optoelectronics Adv Mater Rapid Commun 5(9):1026\u20131029, 2011.","journal-title":"Optoelectronics Adv Mater Rapid Commun"},{"issue":"3","key":"18_CR67","first-page":"1573","volume":"7","author":"FV Celebi","year":"2005","unstructured":"Celebi, F. V., A proposed CAD model based on amplified spontaneous emission spectroscopy. J Optoelectron Adv Mater 7(3):1573\u20131579, 2005.","journal-title":"J Optoelectron Adv Mater"},{"issue":"2","key":"18_CR68","first-page":"87","volume":"12","author":"H Goktas","year":"2007","unstructured":"Goktas, H., Cavusoglu, A., Sen, B., and Toktas, I., The use of artificial neural networks in simulation of mobile ground vehicles. Math Comput Appl. 12(2):87\u201396, 2007.","journal-title":"Math Comput Appl."},{"issue":"4","key":"18_CR69","first-page":"498","volume":"4","author":"N Celebi","year":"2010","unstructured":"Celebi, N., An accurate single CAD model based on radial basis function network. J. Optoelectron. Adv. Mater Rapid Commun. 4(4):498\u2013501, 2010.","journal-title":"J. Optoelectron. Adv. Mater Rapid Commun."},{"issue":"3","key":"18_CR70","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., and Vapnik, V., Support vector networks. Mach Learn 20(3):273\u2013297, 1995.","journal-title":"Mach Learn"},{"issue":"2","key":"18_CR71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-012-9913-4","volume":"37","author":"H Ocak","year":"2013","unstructured":"Ocak, H., A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. J Med Syst 37(2):1\u20139, 2013.","journal-title":"J Med Syst"},{"issue":"1","key":"18_CR72","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L., Random forests. Mach Learn 45(1):5\u201332, 2001.","journal-title":"Mach Learn"},{"key":"18_CR73","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1093\/sleep\/22.5.667","volume":"22","author":"American academy of sleep medicine task force","year":"1999","unstructured":"American academy of sleep medicine task force, Sleep related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep 22:667\u2013689, 1999.","journal-title":"Sleep"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-014-0018-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10916-014-0018-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-014-0018-0","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T00:48:03Z","timestamp":1746146883000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10916-014-0018-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,3]]},"references-count":73,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2014,3]]}},"alternative-id":["18"],"URL":"https:\/\/doi.org\/10.1007\/s10916-014-0018-0","relation":{},"ISSN":["0148-5598","1573-689X"],"issn-type":[{"value":"0148-5598","type":"print"},{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,3]]},"article-number":"18"}}