{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:21:52Z","timestamp":1780460512094,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T00:00:00Z","timestamp":1669507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Immersive Virtual, Augmented and Mixed Reality Center Of Epirus","award":["MIS 5047221"],"award-info":[{"award-number":["MIS 5047221"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalography is one of the most commonly used methods for extracting information about the brain\u2019s condition and can be used for diagnosing epilepsy. The EEG signal\u2019s wave shape contains vital information about the brain\u2019s state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals\u2019 classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden\u2013Fletcher\u2013Goldfarb\u2013Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.<\/jats:p>","DOI":"10.3390\/s22239233","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T08:13:09Z","timestamp":1669623189000},"page":"9233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Evaluating the Window Size\u2019s Role in Automatic EEG Epilepsy Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3231-8852","authenticated-orcid":false,"given":"Vasileios","family":"Christou","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0675-9088","authenticated-orcid":false,"given":"Andreas","family":"Miltiadous","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Tsoulos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6243-3755","authenticated-orcid":false,"given":"Evaggelos","family":"Karvounis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-7005","authenticated-orcid":false,"given":"Katerina D.","family":"Tzimourta","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"},{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-1698","authenticated-orcid":false,"given":"Markos G.","family":"Tsipouras","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50100 Kozani, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Anastasopoulos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Patras, 26504 Rio, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9043-1290","authenticated-orcid":false,"given":"Alexandros T.","family":"Tzallas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0615-783X","authenticated-orcid":false,"given":"Nikolaos","family":"Giannakeas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,27]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2020). Epilepsy, WHO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S24","DOI":"10.1212\/WNL.62.5_suppl_2.S24","article-title":"Special considerations in treating the elderly patient with epilepsy","volume":"62","author":"Ramsay","year":"2004","journal-title":"Neurology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.knosys.2013.02.014","article-title":"Automated EEG analysis of epilepsy: A review","volume":"45","author":"Acharya","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_4","unstructured":"Stevanovic, D. (2012). Automated Epileptic Seizure Detection Methods: A Review Study. Epilepsy, IntechOpen. Chapter 4."},{"key":"ref_5","unstructured":"Cross, D.J., and Cavazos, J.E. (2007). The role of sprouting and plasticity in epileptogenesis and behavior. Behavioral Aspects of Epilepsy, DEMOS."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1111\/j.1528-1157.1997.tb01733.x","article-title":"Patients\u2019 experiences of injury as a result of epilepsy","volume":"38","author":"Buck","year":"1997","journal-title":"Epilepsia"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TBME.2003.821013","article-title":"Dynamical resetting of the human brain at epileptic seizures: Application of nonlinear dynamics and global optimization techniques","volume":"51","author":"Iasemidis","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0013-4694(91)90134-P","article-title":"Slowing of EEG in Parkinson\u2019s disease","volume":"79","author":"Soikkeli","year":"1991","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.clinph.2005.12.007","article-title":"EEG in Creutzfeldt\u2013Jakob disease","volume":"117","author":"Wieser","year":"2006","journal-title":"Clin. Neurophysiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"273","DOI":"10.3389\/fnagi.2016.00273","article-title":"Regularized linear discriminant analysis of EEG features in dementia patients","volume":"8","author":"Neto","year":"2016","journal-title":"Front. Aging Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Tzimourta, K.D., Giannakeas, N., Tsipouras, M.G., Afrantou, T., Ioannidis, P., and Tzallas, A.T. (2021). Alzheimer\u2019s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081437"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103646","DOI":"10.1016\/j.bspc.2022.103646","article-title":"Classification of EEG signals from young adults with dyslexia combining a Brain Computer Interface device and an Interactive Linguistic Software Tool","volume":"76","author":"Christodoulides","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Aspiotis, V., Miltiadous, A., Kalafatakis, K., Tzimourta, K.D., Giannakeas, N., Tsipouras, M.G., Peschos, D., Glavas, E., and Tzallas, A.T. (2022). Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG. Sensors, 22.","DOI":"10.3390\/s22155792"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Aspiotis, V., Sakkas, K., Giannakeas, N., Glavas, E., and Tzallas, A.T. (2022, January 23\u201325). An experimental protocol for exploration of stress in an immersive VR scenario with EEG. Proceedings of the 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece.","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932987"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fix, E., and Hodges, J. (1951). Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties, USAF School of Aviation Medicine, Randolph Field. Technical Report, TX, Tech. Rep. 4.","DOI":"10.1037\/e471672008-001"},{"key":"ref_16","first-page":"1591","article-title":"BFGS method: A new search direction","volume":"43","author":"Hery","year":"2014","journal-title":"Sains Malays."},{"key":"ref_17","first-page":"622","article-title":"Stopping rules for box-constrained stochastic global optimization","volume":"197","author":"Lagaris","year":"2008","journal-title":"Appl. Math. Comput."},{"key":"ref_18","first-page":"598","article-title":"Modifications of real code genetic algorithm for global optimization","volume":"203","author":"Tsoulos","year":"2008","journal-title":"Appl. Math. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","article-title":"Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state","volume":"64","author":"Andrzejak","year":"2001","journal-title":"Phys. Rev. E"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.bspc.2010.01.004","article-title":"Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network","volume":"5","author":"Aghashahi","year":"2010","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","article-title":"Epileptic seizure detection in EEGs using time\u2013frequency analysis","volume":"13","author":"Tzallas","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.eswa.2017.05.052","article-title":"Non-linear classifiers applied to EEG analysis for epilepsy seizure detection","volume":"86","author":"Santofimia","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.cmpb.2016.08.013","article-title":"Automatic identification of epileptic seizures from EEG signals using linear programming boosting","volume":"136","author":"Hassan","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Juarez-Guerra, E., Alarcon-Aquino, V., and Gomez-Gil, P. (2015). Epilepsy seizure detection in EEG signals using wavelet transforms and neural networks. New Trends in Networking, Computing, E-Learning, Systems Sciences, and Engineering, Springer.","DOI":"10.1007\/978-3-319-06764-3_33"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3241056","article-title":"Applying deep learning for epilepsy seizure detection and brain mapping visualization","volume":"15","author":"Hossain","year":"2019","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_26","unstructured":"Shoeb, A.H. (2009). Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.eswa.2011.07.008","article-title":"Detection of epileptic electroencephalogram based on permutation entropy and support vector machines","volume":"39","author":"Nicolaou","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_28","unstructured":"Shoeb, A.H., and Guttag, J.V. (2010, January 21\u201324). Application of machine learning to epileptic seizure detection. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.jneumeth.2010.08.030","article-title":"Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks","volume":"193","author":"Guo","year":"2010","journal-title":"J. Neurosci. Methods"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.eswa.2005.04.007","article-title":"Epileptic seizure detection using dynamic wavelet network","volume":"29","author":"Subasi","year":"2005","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TBME.2007.905490","article-title":"Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection","volume":"55","author":"Adeli","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TBME.2006.886855","article-title":"A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy","volume":"54","author":"Adeli","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/TBME.2007.891945","article-title":"Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection","volume":"54","author":"Adeli","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jneumeth.2010.05.020","article-title":"Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks","volume":"191","author":"Guo","year":"2010","journal-title":"J. Neurosci. Methods"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.cmpb.2016.09.008","article-title":"Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating","volume":"137","author":"Hassan","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_36","unstructured":"Tsoulos, I.G., Gavrilis, D., and Glavas, E. (2005, January 18\u201321). Neural network construction using grammatical evolution. Proceedings of the 5h IEEE International Symposium on Signal Processing and Information Technology, Athens, Greece."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1093\/imamat\/6.1.76","article-title":"The convergence of a class of double-rank minimization algorithms 1. general considerations","volume":"6","author":"Broyden","year":"1970","journal-title":"IMA J. Appl. Math."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1093\/imamat\/6.3.222","article-title":"The convergence of a class of double-rank minimization algorithms: 2. The new algorithm","volume":"6","author":"Broyden","year":"1970","journal-title":"IMA J. Appl. Math."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1093\/comjnl\/13.3.317","article-title":"A new approach to variable metric algorithms","volume":"13","author":"Fletcher","year":"1970","journal-title":"Comput. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1090\/S0025-5718-1970-0258249-6","article-title":"A family of variable-metric methods derived by variational means","volume":"24","author":"Goldfarb","year":"1970","journal-title":"Math. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1090\/S0025-5718-1970-0274029-X","article-title":"Conditioning of quasi-Newton methods for function minimization","volume":"24","author":"Shanno","year":"1970","journal-title":"Math. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2140\/pjm.1966.16.1","article-title":"Minimization of functions having Lipschitz continuous first partial derivatives","volume":"16","author":"Armijo","year":"1966","journal-title":"Pac. J. Math."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1137\/1011036","article-title":"Convergence conditions for ascent methods","volume":"11","author":"Wolfe","year":"1969","journal-title":"SIAM Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1137\/1013035","article-title":"Convergence conditions for ascent methods. II: Some corrections","volume":"13","author":"Wolfe","year":"1971","journal-title":"SIAM Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1137\/0303013","article-title":"On steepest descent","volume":"3","author":"Goldstein","year":"1965","journal-title":"J. Soc. Ind. Appl. Math. Ser. Control"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/BF01589118","article-title":"A tolerant algorithm for linearly constrained optimization calculations","volume":"45","author":"Powell","year":"1989","journal-title":"Math. Program."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0377-0427(00)00425-8","article-title":"Recent developments and trends in global optimization","volume":"124","author":"Pardalos","year":"2000","journal-title":"J. Comput. Appl. Math."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ejor.2005.07.025","article-title":"Integrated crossover rules in real coded genetic algorithms","volume":"176","author":"Kaelo","year":"2007","journal-title":"Eur. J. Oper. Res."},{"key":"ref_49","first-page":"193","article-title":"Genetic algorithms, tournament selection, and the effects of noise","volume":"9","author":"Miller","year":"1995","journal-title":"Complex Syst."},{"key":"ref_50","first-page":"71","article-title":"Genetic algorithms+data structures=evolution programs","volume":"18","author":"Michalewicz","year":"1996","journal-title":"Math. Intell."},{"key":"ref_51","unstructured":"Wang, J.G. (2005, January 18\u201321). An adaptive nearest neighbor algorithm for classification. Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"276589","DOI":"10.1155\/2014\/276589","article-title":"A machine learning approach for specification of spinal cord injuries using fractional anisotropy values obtained from diffusion tensor images","volume":"2014","author":"Tay","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.48084\/etasr.2031","article-title":"Evaluation of window size in classification of epileptic short-term EEG signals using a Brain Computer Interface software","volume":"8","author":"Tzimourta","year":"2018","journal-title":"Eng. Technol. Appl. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"066017","DOI":"10.1088\/1741-2552\/ac9c93","article-title":"Improving automated diagnosis of epilepsy from EEGs beyond IEDs","volume":"19","author":"Thangavel","year":"2022","journal-title":"J. Neural Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9233\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:27:54Z","timestamp":1760146074000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,27]]},"references-count":54,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239233"],"URL":"https:\/\/doi.org\/10.3390\/s22239233","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,27]]}}}