{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T17:36:34Z","timestamp":1779298594146,"version":"3.51.4"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T00:00:00Z","timestamp":1618358400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T00:00:00Z","timestamp":1618358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128\u2009\u00d7\u20098\u2009=\u20091024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model.<\/jats:p>","DOI":"10.1007\/s11042-021-10882-4","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T07:06:56Z","timestamp":1618470416000},"page":"25197-25218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals"],"prefix":"10.1007","volume":"80","author":[{"given":"T\u00fcrker","family":"Tuncer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ganesh R.","family":"Naik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-2801","authenticated-orcid":false,"given":"Pawe\u0142","family":"P\u0142awiak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,14]]},"reference":[{"key":"10882_CR1","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1108\/LHT-01-2017-0030","volume":"35","author":"M Abdar","year":"2017","unstructured":"Abdar M, Yen NY (2017) Understanding regional characteristics through crowd preference and confidence mining in P2P accommodation rental service. Library Hi Tech 35:521\u2013541","journal-title":"Library Hi Tech"},{"key":"10882_CR2","unstructured":"Abdar M, Zomorodi-Moghadam M, Zhou X, Gururajan R, Tao X, Barua PD, et al. (2018) A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recognition Letters"},{"key":"10882_CR3","doi-asserted-by":"crossref","unstructured":"Acharya UR, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL et al (2018) Characterization of focal EEG signals: a review. Futur Gener Comput Syst","DOI":"10.1016\/j.future.2018.08.044"},{"key":"10882_CR4","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","volume":"100","author":"UR Acharya","year":"2018","unstructured":"Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270\u2013278","journal-title":"Comput Biol Med"},{"key":"10882_CR5","doi-asserted-by":"crossref","unstructured":"Adhikary DD, Gupta D (2020) Applying over 100 classifiers for churn prediction in telecom companies. Multimed Tools Appl:1\u201322","DOI":"10.1007\/s11042-020-09658-z"},{"key":"10882_CR6","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s40708-016-0039-1","volume":"3","author":"HR Al Ghayab","year":"2016","unstructured":"Al Ghayab HR, Li Y, Abdulla S, Diykh M, Wan X (2016) Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain informatics 3:85\u201391","journal-title":"Brain informatics"},{"key":"10882_CR7","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.bspc.2018.10.004","volume":"48","author":"W Al-Salman","year":"2019","unstructured":"Al-Salman W, Li Y, Wen P (2019) Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features. Biomedical Signal Processing and Control. 48:80\u201392","journal-title":"Biomedical Signal Processing and Control."},{"key":"10882_CR8","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak RG, Lehnertz K, Mormann F, Rieke C, 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":"10882_CR9","doi-asserted-by":"crossref","unstructured":"Anuragi A, Sisodia DS (2018) Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform. Biomedical Signal Processing and Control","DOI":"10.1016\/j.bspc.2018.10.017"},{"issue":"7","key":"10882_CR10","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1088\/0967-3334\/27\/7\/004","volume":"27","author":"P Augustyniak","year":"2006","unstructured":"Augustyniak P, Tadeusiewicz R (2006) Assessment of electrocardiogram visual interpretation strategy based on scanpath analysis. Physiological Measurement 27(7):597\u2013608","journal-title":"Physiological Measurement"},{"key":"10882_CR11","doi-asserted-by":"crossref","unstructured":"Ayyad SM, Saleh AI, Labib LM (2019) Gene expression cancer classification using modified k-nearest neighbors technique. Biosystems","DOI":"10.1016\/j.biosystems.2018.12.009"},{"key":"10882_CR12","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1007\/s00521-015-1961-5","volume":"27","author":"S Balasundaram","year":"2016","unstructured":"Balasundaram S, Gupta D (2016) Knowledge-based extreme learning machines. Neural Comput & Applic 27:1629\u20131641","journal-title":"Neural Comput & Applic"},{"key":"10882_CR13","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.neucom.2016.10.077","volume":"250","author":"RA Becerra-Garc\u00eda","year":"2017","unstructured":"Becerra-Garc\u00eda RA, Garc\u00eda-Berm\u00fadez R, Joya-Caparr\u00f3s G, Fern\u00e1ndez-Higuera A, Vel\u00e1zquez-Rodr\u00edguez C, Vel\u00e1zquez-Mari\u00f1o M et al (2017) Data mining process for identification of non-spontaneous saccadic movements in clinical electrooculography. Neurocomputing. 250:28\u201336","journal-title":"Neurocomputing."},{"key":"10882_CR14","doi-asserted-by":"crossref","unstructured":"Borah P, Gupta D (2019) Functional iterative approaches for solving support vector classification problems based on generalized Huber loss. Neural Comput & Applic:1\u201321","DOI":"10.1007\/s00521-019-04436-x"},{"key":"10882_CR15","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1007\/s10489-019-01596-0","volume":"50","author":"P Borah","year":"2020","unstructured":"Borah P, Gupta D (2020) Unconstrained convex minimization based implicit Lagrangian twin extreme learning machine for classification (ULTELMC). Appl Intell 50:1327\u20131344","journal-title":"Appl Intell"},{"key":"10882_CR16","doi-asserted-by":"crossref","unstructured":"Das A, Acharya UR, Panda SS, Sabut S (2018) Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cognitive Systems Research.","DOI":"10.1016\/j.cogsys.2018.12.009"},{"key":"10882_CR17","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.eswa.2018.08.031","volume":"114","author":"H Dose","year":"2018","unstructured":"Dose H, M\u00f8ller JS, Iversen HK, Puthusserypady S (2018) An end-to-end deep learning approach to MI-EEG signal classification for BCIs. Expert Syst Appl 114:532\u2013542","journal-title":"Expert Syst Appl"},{"key":"10882_CR18","volume-title":"Yeh S-C","author":"N Duan","year":"2018","unstructured":"Duan N, Liu L-Z, Yu X-J, Li Q (2018) Yeh S-C. Classification of multichannel surface-electromyography signals based on convolutional neural networks, Journal of Industrial Information Integration"},{"key":"10882_CR19","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.3390\/en6041887","volume":"6","author":"G-F Fan","year":"2013","unstructured":"Fan G-F, Qing S, Wang H, Hong W-C, Li H-J (2013) Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies. 6:1887\u20131901","journal-title":"Energies."},{"key":"10882_CR20","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1016\/j.neucom.2015.08.051","volume":"173","author":"G-F Fan","year":"2016","unstructured":"Fan G-F, Peng L-L, Hong W-C, Sun F (2016) Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing. 173:958\u2013970","journal-title":"Neurocomputing."},{"key":"10882_CR21","doi-asserted-by":"crossref","unstructured":"Fan G-F, Wei X, Li Y-T, Hong W-C (2020) Forecasting electricity consumption using a novel hybrid model. Sustain Cities Soc 102320","DOI":"10.1016\/j.scs.2020.102320"},{"key":"10882_CR22","doi-asserted-by":"crossref","unstructured":"Fan GF, Guo YH, Zheng JM, Hong WC. (2020) A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back-propagation neural network for mid-short-term load forecasting. Journal of Forecasting","DOI":"10.1002\/for.2655"},{"key":"10882_CR23","first-page":"108","volume":"2","author":"T Fathima","year":"2011","unstructured":"Fathima T, Bedeeuzzaman M, Farooq O, Khan YU (2011) Wavelet based features for epileptic seizure detection. MES Journal of Technology and Management 2:108\u2013112","journal-title":"MES Journal of Technology and Management"},{"key":"10882_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","volume":"161","author":"O Faust","year":"2018","unstructured":"Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Prog Biomed 161:1\u201313","journal-title":"Comput Methods Prog Biomed"},{"key":"10882_CR25","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.advms.2018.08.003","volume":"64","author":"I Gruszczy\u0144ska","year":"2019","unstructured":"Gruszczy\u0144ska I, Mosdorf R, Sobaniec P, \u017bochowska-Sobaniec M, Borowska M (2019) Epilepsy identification based on EEG signal using RQA method. Advances in medical sciences 64:58\u201364","journal-title":"Advances in medical sciences"},{"key":"10882_CR26","doi-asserted-by":"crossref","unstructured":"Gupta D, Borah P, Prasad M (2017) A fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM). 2017 IEEE symposium series on computational intelligence (SSCI): IEEE. p. 1\u20137.","DOI":"10.1109\/SSCI.2017.8280964"},{"key":"10882_CR27","doi-asserted-by":"crossref","unstructured":"Gupta D, Sarma HJ, Mishra K, Prasad M. (2019) Regularized Universum twin support vector machine for classification of EEG Signal. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC): IEEE. p. 2298\u2013304.","DOI":"10.1109\/SMC.2019.8913897"},{"key":"10882_CR28","unstructured":"Hammad M, P\u0142awiak P, Wang K, Acharya UR ResNet-attention model for human authentication using ECG signals. Wiley Expert Systems, p e12547"},{"key":"10882_CR29","doi-asserted-by":"crossref","unstructured":"Hassoon M, Kouhi MS, Zomorodi-Moghadam M, Abdar M (2017) Rule optimization of boosted C5. 0 Classification using genetic algorithm for liver disease prediction. Computer and Applications (ICCA), 2017 International Conference on: IEEE. p. 299\u2013305.","DOI":"10.1109\/COMAPP.2017.8079783"},{"key":"10882_CR30","doi-asserted-by":"publisher","first-page":"106626","DOI":"10.1016\/j.asoc.2020.106626","volume":"96","author":"BB Hazarika","year":"2020","unstructured":"Hazarika BB, Gupta D (2020) Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks. Appl Soft Comput 96:106626","journal-title":"Appl Soft Comput"},{"key":"10882_CR31","doi-asserted-by":"crossref","unstructured":"Hazarika B, Gupta D, Berlin M (2020) A coiflet LDMR and coiflet OB-ELM for river suspended sediment load prediction. Int J Environ Sci Technol:1\u201318","DOI":"10.1007\/s13762-020-02967-8"},{"key":"10882_CR32","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.3390\/en12061093","volume":"12","author":"W-C Hong","year":"2019","unstructured":"Hong W-C, Fan G-F (2019) Hybrid empirical mode decomposition with support vector regression model for short term load forecasting. Energies. 12:1093","journal-title":"Energies."},{"key":"10882_CR33","doi-asserted-by":"publisher","first-page":"23","DOI":"10.5121\/ijsc.2014.5303","volume":"5","author":"SJ Husain","year":"2014","unstructured":"Husain SJ, Rao K (2014) An artificial neural network model for classification of epileptic seizures using Huang-Hilbert transform. International Journal on Soft Computing 5:23\u201333","journal-title":"International Journal on Soft Computing"},{"key":"10882_CR34","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.bbe.2017.08.006","volume":"38","author":"S Ibrahim","year":"2018","unstructured":"Ibrahim S, Djemal R, Alsuwailem A (2018) Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybernetics and Biomedical Engineering 38:16\u201326","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"10882_CR35","doi-asserted-by":"publisher","first-page":"72650","DOI":"10.1109\/ACCESS.2020.2988160","volume":"8","author":"C Iwendi","year":"2020","unstructured":"Iwendi C, Jalil Z, Javed AR, Reddy T, Kaluri R, Srivastava G et al (2020) KeySplitWatermark: zero watermarking algorithm for software protection against cyber-attacks. IEEE Access 8:72650\u201372660","journal-title":"IEEE Access"},{"key":"10882_CR36","doi-asserted-by":"crossref","unstructured":"Izonin I, Tkachenko R, Kryvinska N, Tkachenko P (2019) Multiple linear regression based on coefficients identification using non-iterative SGTM Neural-Like Structure. International Work-Conference on Artificial Neural Networks: Springer. p. 467\u2013479.","DOI":"10.1007\/978-3-030-20521-8_39"},{"key":"10882_CR37","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.medengphy.2017.01.015","volume":"41","author":"X Jiang","year":"2017","unstructured":"Jiang X, Merhi L-K, Xiao ZG, Menon C (2017) Exploration of force myography and surface electromyography in hand gesture classification. Med Eng Phys 41:63\u201373","journal-title":"Med Eng Phys"},{"issue":"23","key":"10882_CR38","doi-asserted-by":"publisher","first-page":"5079","DOI":"10.3390\/s19235079","volume":"19","author":"RNVPS Kandala","year":"2019","unstructured":"Kandala RNVPS, Dhuli R, P\u0142awiak P, Naik G, Moeinzadeh H, Gargiulo GD, Gunnam S (2019) Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method. MDPI Sensors 19(23):5079","journal-title":"MDPI Sensors"},{"key":"10882_CR39","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/s13246-015-0362-5","volume":"38","author":"Y Kaya","year":"2015","unstructured":"Kaya Y (2015) Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis. Australasian physical & engineering sciences in medicine 38:435\u2013446","journal-title":"Australasian physical & engineering sciences in medicine"},{"key":"10882_CR40","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s13246-018-0669-0","volume":"41","author":"Y Kaya","year":"2018","unstructured":"Kaya Y, Ertu\u011frul \u00d6F (2018) A stable feature extraction method in classification epileptic EEG signals. Australasian physical & engineering sciences in medicine. 41:721\u2013730","journal-title":"Australasian physical & engineering sciences in medicine."},{"key":"10882_CR41","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.amc.2014.05.128","volume":"243","author":"Y Kaya","year":"2014","unstructured":"Kaya Y, Uyar M, Tekin R, Y\u0131ld\u0131r\u0131m S (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl Math Comput 243:209\u2013219","journal-title":"Appl Math Comput"},{"key":"10882_CR42","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.eswa.2017.07.020","volume":"88","author":"O Kocadagli","year":"2017","unstructured":"Kocadagli O, Langari R (2017) Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations. Expert Syst Appl 88:419\u2013434","journal-title":"Expert Syst Appl"},{"key":"10882_CR43","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 M, Anand R (2014) Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing. 133:271\u2013279","journal-title":"Neurocomputing."},{"key":"10882_CR44","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neucom.2018.06.068","volume":"314","author":"Y Li","year":"2018","unstructured":"Li Y, Pang Y, Wang J, Li X (2018) Patient-specific ECG classification by deeper CNN from generic to dedicated. Neurocomputing. 314:336\u2013346","journal-title":"Neurocomputing."},{"key":"10882_CR45","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1007\/s11071-019-05149-5","volume":"97","author":"M-W Li","year":"2019","unstructured":"Li M-W, Geng J, Hong W-C, Zhang L-D (2019) Periodogram estimation based on LSSVR-CCPSO compensation for forecasting ship motion. Nonlinear Dynamics. 97:2579\u20132594","journal-title":"Nonlinear Dynamics."},{"key":"10882_CR46","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neuroimage.2007.02.060","volume":"37","author":"RA Masterton","year":"2007","unstructured":"Masterton RA, Abbott DF, Fleming SW, Jackson GD (2007) Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings. Neuroimage. 37:202\u2013211","journal-title":"Neuroimage."},{"key":"10882_CR47","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.bspc.2013.12.003","volume":"10","author":"S Motamedi-Fakhr","year":"2014","unstructured":"Motamedi-Fakhr S, Moshrefi-Torbati M, Hill M, Hill CM, White PR (2014) Signal processing techniques applied to human sleep EEG signals\u2014a review. Biomedical Signal Processing and Control 10:21\u201333","journal-title":"Biomedical Signal Processing and Control"},{"key":"10882_CR48","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.bspc.2017.08.023","volume":"40","author":"AY Mutlu","year":"2018","unstructured":"Mutlu AY (2018) Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition. Biomedical Signal Processing and Control. 40:33\u201340","journal-title":"Biomedical Signal Processing and Control."},{"key":"10882_CR49","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.bspc.2018.08.030","volume":"47","author":"N Nazmi","year":"2019","unstructured":"Nazmi N, Rahman MAA, Yamamoto S-I, Ahmad SA (2019) Walking gait event detection based on electromyography signals using artificial neural network. Biomedical Signal Processing and Control. 47:334\u2013343","journal-title":"Biomedical Signal Processing and Control."},{"key":"10882_CR50","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.eswa.2011.07.008","volume":"39","author":"N Nicolaou","year":"2012","unstructured":"Nicolaou N, Georgiou J (2012) Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39:202\u2013209","journal-title":"Expert Syst Appl"},{"key":"10882_CR51","doi-asserted-by":"publisher","first-page":"13475","DOI":"10.1016\/j.eswa.2011.04.149","volume":"38","author":"U Orhan","year":"2011","unstructured":"Orhan U, Hekim M, Ozer M (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38:13475\u201313481","journal-title":"Expert Syst Appl"},{"key":"10882_CR52","doi-asserted-by":"publisher","first-page":"105740","DOI":"10.1016\/j.asoc.2019.105740","volume":"84","author":"P P\u0142awiak","year":"2019","unstructured":"P\u0142awiak P, Abdar M (2019) UR Acharya; application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring. Elsevier Applied Soft Computing 84:105740","journal-title":"Elsevier Applied Soft Computing"},{"key":"10882_CR53","doi-asserted-by":"crossref","unstructured":"P P\u0142awiak, M Abdar, J P\u0142awiak, V Makarenkov, UR Acharya (2020) DGHNL: a new deep genetic hierarchical network of learners for prediction of credit scoring; Elsevier Information Sciences 516, 401\u2013418","DOI":"10.1016\/j.ins.2019.12.045"},{"key":"10882_CR54","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.eswa.2018.03.053","volume":"106","author":"B Richhariya","year":"2018","unstructured":"Richhariya B, Tanveer M (2018) EEG signal classification using universum support vector machine. Expert Syst Appl 106:169\u2013182","journal-title":"Expert Syst Appl"},{"key":"10882_CR55","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","volume":"30","author":"M Sajjad","year":"2019","unstructured":"Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. Journal of Computational Science 30:174\u2013182","journal-title":"Journal of Computational Science"},{"key":"10882_CR56","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.ifacol.2016.07.160","volume":"49","author":"M Schimmack","year":"2016","unstructured":"Schimmack M, Nguyen S, Mercorelli P (2016) Anatomy of Haar wavelet filter and its implementation for signal processing. IFAC-PapersOnLine. 49:99\u2013104","journal-title":"IFAC-PapersOnLine."},{"key":"10882_CR57","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.compbiomed.2018.07.005","volume":"102","author":"M Sharma","year":"2018","unstructured":"Sharma M, San Tan R, Acharya UR (2018) A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput Biol Med 102:341\u2013356","journal-title":"Comput Biol Med"},{"key":"10882_CR58","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1049\/iet-smt.2018.5358","volume":"13","author":"S Siuly","year":"2018","unstructured":"Siuly S, Alcin OF, Bajaj V, Sengur A, Zhang Y (2018) Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure. IET Science, Measurement & Technology 13:35\u201341","journal-title":"IET Science, Measurement & Technology"},{"issue":"1\u20133","key":"10882_CR59","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.neucom.2008.01.003","volume":"72","author":"M Szaleniec","year":"2008","unstructured":"Szaleniec M, Tadeusiewicz R, Witko M (2008) How to select an optimal neural model of chemical reactivity? Neurocomputing 72(1\u20133) Special Issue: SI:241\u2013256","journal-title":"Neurocomputing"},{"key":"10882_CR60","doi-asserted-by":"crossref","unstructured":"Szaleniec, Joanna; Wiatr, Maciej; Szaleniec, Maciej; et al.; Artificial neural network modelling of the results of tympanoplasty in chronic suppurative otitis media patients; Computers In Biology And Medicine Volume: 43 Issue: 1 Pages: 16\u201322 Published: JAN 1 2013","DOI":"10.1016\/j.compbiomed.2012.10.003"},{"key":"10882_CR61","doi-asserted-by":"crossref","unstructured":"Tkachenko R, Izonin I (2018) Model and principles for the implementation of neural-like structures based on geometric data transformations. International Conference on Computer Science, Engineering and Education Applications: Springer. p. 578\u201387.","DOI":"10.1007\/978-3-319-91008-6_58"},{"key":"10882_CR62","doi-asserted-by":"crossref","unstructured":"Tkachenko R, Tkachenko P, Izonin I, Tsymbal Y. (2018) Learning-based image scaling using neural-like structure of geometric transformation paradigm. Advances in Soft Computing and Machine Learning in Image Processing: Springer. p. 537\u2013565.","DOI":"10.1007\/978-3-319-63754-9_25"},{"key":"10882_CR63","doi-asserted-by":"crossref","unstructured":"Tkachenko R, Doroshenko A, Izonin I, Tsymbal Y, Havrysh B. (2018) Imbalance data classification via neural-like structures of geometric transformations model: local and global approaches. International conference on computer science, engineering and education applications: Springer. p. 112\u201322.","DOI":"10.1007\/978-3-319-91008-6_12"},{"key":"10882_CR64","doi-asserted-by":"crossref","unstructured":"Tripathy R, Acharya UR. (2018) Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework. Biocybernetics and Biomedical Engineering.","DOI":"10.1016\/j.bbe.2018.05.005"},{"key":"10882_CR65","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.measurement.2018.04.002","volume":"123","author":"SA Tuncer","year":"2018","unstructured":"Tuncer SA, Alkan A (2018) A decision support system for detection of the renal cell cancer in the kidney. Measurement. 123:298\u2013303","journal-title":"Measurement."},{"key":"10882_CR66","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1007\/s11227-020-03205-1","volume":"76","author":"T Tuncer","year":"2020","unstructured":"Tuncer T, Ertam F, Dogan S, Aydemir E, P\u0142awiak P (2020) Ensemble residual networks based gender and activity recognition method with signals. Springer The Journal of Supercomputing 76:2119\u20132138","journal-title":"Springer The Journal of Supercomputing"},{"key":"10882_CR67","doi-asserted-by":"crossref","unstructured":"Tzimourta KD, Tzallas AT, Giannakeas N, Astrakas LG, Tsalikakis DG, Angelidis P et al (2018) A robust methodology for classification of epileptic seizures in EEG signals. Heal Technol:1\u20138","DOI":"10.48084\/etasr.2031"},{"key":"10882_CR68","doi-asserted-by":"crossref","unstructured":"Wang H, Zhuo G, Zhang Y (2016) Analyzing EEG signal data for detection of epileptic seizure: introducing weight on visibility graph with complex network feature. Australasian Database Conference: Springer. p. 56\u201366.","DOI":"10.1007\/978-3-319-46922-5_5"},{"key":"10882_CR69","unstructured":"WHO 2017: https:\/\/www.who.int\/mental_health\/en\/; Date of access: January 25, 2019."},{"key":"10882_CR70","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.cmpb.2018.05.026","volume":"162","author":"CM Yilmaz","year":"2018","unstructured":"Yilmaz CM, Kose C, Hatipoglu B (2018) A quasi-probabilistic distribution model for EEG signal classification by using 2-D signal representation. Comput Methods Prog Biomed 162:187\u2013196","journal-title":"Comput Methods Prog Biomed"},{"key":"10882_CR71","volume-title":"Ding S","author":"Z Zhang","year":"2020","unstructured":"Zhang Z (2020) Ding S. Sun Y. A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task, Neurocomputing"},{"key":"10882_CR72","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s11071-019-05252-7","volume":"98","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Hong W-C (2019) Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dynamics 98:1107\u20131136","journal-title":"Nonlinear Dynamics"},{"key":"10882_CR73","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.cmpb.2014.04.001","volume":"115","author":"G Zhu","year":"2014","unstructured":"Zhu G, Li Y, Wen PP (2014) Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm. Comput Methods Prog Biomed 115:64\u201375","journal-title":"Comput Methods Prog Biomed"},{"key":"10882_CR74","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.bios.2014.09.006","volume":"67","author":"Y Zilberman","year":"2015","unstructured":"Zilberman Y, Sonkusale SR (2015) Microfluidic optoelectronic sensor for salivary diagnostics of stomach cancer. Biosens Bioelectron 67:465\u2013471","journal-title":"Biosens Bioelectron"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10882-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10882-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10882-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T03:59:15Z","timestamp":1698897555000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10882-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,14]]},"references-count":74,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10882"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10882-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,14]]},"assertion":[{"value":"12 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2021","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 declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}