{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:03:12Z","timestamp":1783008192418,"version":"3.54.5"},"reference-count":93,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T00:00:00Z","timestamp":1579824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of Education of Humanities and Social Science Project","award":["19YJAZH047"],"award-info":[{"award-number":["19YJAZH047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost), named CEEMD-XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (IMFs) and residues. Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB-MIT (Children\u2019s Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD-XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy.<\/jats:p>","DOI":"10.3390\/e22020140","type":"journal-article","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T11:01:00Z","timestamp":1579863660000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting"],"prefix":"10.3390","volume":"22","author":[{"given":"Jiang","family":"Wu","sequence":"first","affiliation":[{"name":"School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China"},{"name":"Sichuan Province Key Laboratory of Financial Intelligence and Financial Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengfei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1546-8015","authenticated-orcid":false,"given":"Taiyong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China"},{"name":"Sichuan Province Key Laboratory of Financial Intelligence and Financial Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1093\/brain\/awl241","article-title":"Seizure prediction: The long and winding road","volume":"130","author":"Mormann","year":"2007","journal-title":"Brain"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1212\/WNL.0b013e31822cfc90","article-title":"Incidence of epilepsy: A systematic review and meta-analysis","volume":"77","author":"Ngugi","year":"2011","journal-title":"Neurology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"80510","DOI":"10.1155\/2007\/80510","article-title":"Automatic seizure detection based on time-frequency analysis and artificial neural networks","volume":"2007","author":"Tzallas","year":"2007","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_4","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. Meth. Programs Biomed."},{"key":"ref_5","unstructured":"WHO\/ILAE\/IBE (2019, May 29). Epilepsy Management at Primary Health Level in Rural China. Available online: https:\/\/www.who.int\/mental_health\/publications\/epilepsy_management_rural_china\/en\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2991\/ijcis.11.1.51","article-title":"A computer aided analysis scheme for detecting epileptic seizure from EEG data","volume":"11","author":"Kabir","year":"2018","journal-title":"Int. J. Comput. Int. Sys."},{"key":"ref_7","unstructured":"Supriya, S., Siuly, S., Wang, H., and Zhang, Y. (2018). EEG Sleep Stages Analysis and Classification Based on Weighed Complex Network Features. IEEE Trans. Emerg. Top. Comput. Intell., 1\u201311."},{"key":"ref_8","first-page":"297","article-title":"A new design of mental state classification for subject independent BCI systems","volume":"40","author":"Joadder","year":"2019","journal-title":"Inno. Res. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s10916-009-9379-1","article-title":"Analysis of EEG Signals under Flash Stimulation for Migraine and Epileptic Patients","volume":"35","author":"Akben","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","article-title":"Epileptic seizure detection in EEGs using time-frequency analysis","volume":"13","author":"Tzallas","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1088\/1741-2560\/4\/2\/012","article-title":"Amplitude and phase coupling measures for feature extraction in an EEG-based brain\u2013computer interface","volume":"4","author":"Wei","year":"2007","journal-title":"J. Neural Eng."},{"key":"ref_12","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":"Samanwoy","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2013.08.006","article-title":"Classification of ictal and seizure-free EEG signals using fractional linear prediction","volume":"9","author":"Joshi","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/0013-4694(91)90128-Q","article-title":"Computerized seizure detection of complex partial seizures","volume":"79","author":"Murro","year":"1991","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_15","first-page":"1017","article-title":"Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform","volume":"187","author":"Polat","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1007\/s10916-010-9433-z","article-title":"Application of higher order spectra to identify epileptic EEG","volume":"35","author":"Chua","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.neunet.2019.12.006","article-title":"A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia","volume":"123","author":"Ieracitano","year":"2020","journal-title":"Neural Networks"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15491","DOI":"10.1038\/s41598-018-33969-9","article-title":"Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction","volume":"8","author":"Gagliano","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1016\/j.clinph.2011.03.022","article-title":"Determination of awareness in patients with severe brain injury using EEG power spectral analysis","volume":"122","author":"Goldfine","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.dsp.2008.07.007","article-title":"Analysis of EEG signals by implementing eigenvector methods\/recurrent neural networks","volume":"19","year":"2009","journal-title":"Digit. Signal Prog."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s00521-016-2646-4","article-title":"A novel approach for automated detection of focal EEG signals using empirical wavelet transform","volume":"29","author":"Bhattacharyya","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, T., and Zhou, M. (2016). ECG classification using wavelet packet entropy and random forests. Entropy, 18.","DOI":"10.3390\/e18080285"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s11063-016-9530-1","article-title":"Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition","volume":"45","author":"Zhang","year":"2017","journal-title":"Neural Process. Lett."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.bspc.2017.05.008","article-title":"Stockwell transform for epileptic seizure detection from EEG signals","volume":"38","author":"Kalbkhani","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.cmpb.2013.11.014","article-title":"Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions","volume":"113","author":"Pachori","year":"2014","journal-title":"Comput. Meth. Programs Biomed."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/JBHI.2014.2387795","article-title":"A Novel Method for Automated Diagnosis of Epilepsy using Complex-Valued Classifiers","volume":"20","author":"Peker","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.eswa.2005.04.011","article-title":"Recurrent neural networks employing Lyapunov exponents for EEG signals classification","volume":"29","year":"2005","journal-title":"Expert Syst. Appl."},{"key":"ref_29","first-page":"572","article-title":"A new framework based on recurrence quantification analysis for epileptic seizure detection","volume":"11","author":"Niknazar","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","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":"Samanwoy","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1109\/TSMCB.2012.2229269","article-title":"A novel decision-tree method for structured continuous-label classification","volume":"43","author":"Hu","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neucom.2017.02.053","article-title":"Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier","volume":"241","author":"Mursalin","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.eswa.2017.07.020","article-title":"Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations","volume":"88","author":"Kocadagli","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"141","DOI":"10.3233\/BME-171663","article-title":"Epileptic seizure detection in EEG signal with GModPCA and support vector machine","volume":"28","author":"Jaiswal","year":"2017","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6849360","DOI":"10.1155\/2017\/6849360","article-title":"Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification","volume":"2017","author":"Wang","year":"2017","journal-title":"Comput. Math. Method Med."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shoaran, M., Farivar, M., and Emami, A. (2016, January 16). Hardware-friendly seizure detection with a boosted ensemble of shallow decision trees. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591074"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/JETCAS.2018.2844733","article-title":"Energy-Efficient Classification for Resource-Constrained Biomedical Applications","volume":"8","author":"Shoaran","year":"2018","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1080\/21681163.2016.1141062","article-title":"Convolutional neural networks for real-time epileptic seizure detection","volume":"6","author":"Achilles","year":"2016","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.05.019","article-title":"A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals","volume":"99","author":"Tsiouris","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1007\/s11063-018-9919-0","article-title":"Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine","volume":"50","author":"Ren","year":"2019","journal-title":"Neural Process. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for Electroencephalogram (EEG) classification tasks: A review","volume":"16","author":"Craik","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4387","DOI":"10.1007\/s00500-016-2071-8","article-title":"A novel collaborative optimization algorithm in solving complex optimization problems","volume":"21","author":"Deng","year":"2017","journal-title":"Soft Comput."},{"key":"ref_43","unstructured":"Zhang, S., Zhao, H., Xu, J., and Deng, W. (2019). A novel fault diagnosis method based on improved adaptive VMD energy entropy and PNN. Trans. Can. Soc. Mech. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhao, H., Liu, H., Xu, J., and Deng, W. (2019). Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine. IEEE Trans. Instrum. Meas.","DOI":"10.1109\/TIM.2019.2948414"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"99263","DOI":"10.1109\/ACCESS.2019.2929094","article-title":"Fault diagnosis method based on principal component analysis and broad learning system","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, T., Zhou, Y., Li, X., Wu, J., and He, T. (2019). Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors. Energies, 12.","DOI":"10.3390\/en12193603"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1109\/TMC.2017.2753244","article-title":"Demographic Information Inference through Meta-Data Analysis of Wi-Fi Traffic","volume":"17","author":"Li","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1002\/mp.12842","article-title":"A machine learning approach to the accurate prediction of monitor units for a compact proton machine","volume":"45","author":"Sun","year":"2018","journal-title":"Med Phys."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.solener.2017.05.018","article-title":"Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boosting","volume":"150","author":"Aler","year":"2017","journal-title":"Solar Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"0619071","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_51","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, T., Zhou, M., Guo, C., Luo, M., Wu, J., Pan, F., Tao, Q., and He, T. (2016). Forecasting crude oil price using EEMD and RVM with adaptive PSO-based kernels. Energies, 9.","DOI":"10.3390\/en9121014"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22\u201327). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wu, J., Chen, Y., Zhou, T., and Li, T. (2019). An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting. Energies, 12.","DOI":"10.3390\/en12071239"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Singh, G., Kaur, M., and Singh, D. (2015, January 21\u201322). Detection of epileptic seizure using wavelet transformation and spike based features. Proceedings of the International Conference on Recent Advances in Engineering & Computational Sciences, Chandigarh, India.","DOI":"10.1109\/RAECS.2015.7453376"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4392785","DOI":"10.1155\/2019\/4392785","article-title":"A CEEMDAN and XGBOOST-based approach to forecast crude oil prices","volume":"2019","author":"Zhou","year":"2019","journal-title":"Complexity"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, T., Shi, J., Li, X., Wu, J., and Pan, F. (2019). Image encryption based on pixel-level diffusion with dynamic filtering and DNA-level permutation with 3D Latin cubes. Entropy, 21.","DOI":"10.3390\/e21030319"},{"key":"ref_62","first-page":"7485621","article-title":"Image encryption based on dynamic filtering and bit cuboid operations","volume":"2019","author":"Li","year":"2019","journal-title":"Complexity"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wu, J., Shi, J., and Li, T. (2020). A Novel Image Encryption Approach Based on a Hyperchaotic System, Pixel-Level Filtering with Variable Kernels, and DNA-Level Diffusion. Entropy, 22.","DOI":"10.3390\/e22010005"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neucom.2018.03.067","article-title":"Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh\u2014A Python package)","volume":"307","author":"Christ","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.cmpb.2005.06.012","article-title":"Entropies for detection of epilepsy in EEG","volume":"80","author":"Kannathal","year":"2005","journal-title":"Comput. Meth. Programs Biomed."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Christoph","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.3390\/e17085218","article-title":"An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures","volume":"17","author":"Sharma","year":"2015","journal-title":"Entropy"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1093\/bja\/aeh251","article-title":"Spectral entropy measurement of patient responsiveness during propofol and remifentanil. A comparison with the bispectral index","volume":"93","author":"Vanluchene","year":"2004","journal-title":"Br. J. Anaesth."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1007\/s11517-019-01951-w","article-title":"Grasshopper optimization algorithm\u2013based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals","volume":"57","author":"Singh","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xun, G., Jia, X., and Zhang, A. (2016). Detecting epileptic seizures with electroencephalogram via a context-learning model. BMC Med. Inform. Decis, 16.","DOI":"10.1186\/s12911-016-0310-7"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s12918-018-0626-2","article-title":"A multi-context learning approach for EEG epileptic seizure detection","volume":"12","author":"Yuan","year":"2018","journal-title":"BMC Syst. Biol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.neucom.2013.11.009","article-title":"Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine","volume":"133","author":"Kumar","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"7716","DOI":"10.1109\/ACCESS.2016.2585661","article-title":"DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers","volume":"4","author":"Sharmila","year":"2016","journal-title":"IEEE Access"},{"key":"ref_75","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. Meth. Programs Biomed."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.eswa.2016.02.040","article-title":"A novel robust diagnostic model to detect seizures in electroencephalography","volume":"56","author":"Swami","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.compeleceng.2015.09.001","article-title":"A hybrid automated detection of epileptic seizures in EEG records","volume":"53","author":"Tawfik","year":"2016","journal-title":"Comput. Electr. Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.patrec.2017.03.023","article-title":"A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension","volume":"94","author":"Sharma","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.bspc.2017.01.005","article-title":"Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals","volume":"34","author":"Jaiswal","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/JBHI.2016.2589971","article-title":"Automated Diagnosis of Epilepsy using Key-point Based Local Binary Pattern of EEG Signals","volume":"21","author":"Tiwari","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1007\/s40846-017-0275-8","article-title":"Classification of Seizure Prone EEG Signal Using Amplitude and Frequency Based Parameters of Intrinsic Mode Functions","volume":"37","author":"Kaur","year":"2017","journal-title":"J. Med. Biol. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1850003","DOI":"10.1142\/S012906571850003X","article-title":"Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features","volume":"28","author":"Li","year":"2018","journal-title":"Int. J. Neural Syst."},{"key":"ref_83","unstructured":"Singh, N., and Dehuri, S. (2017, January 11\u201312). Usage of Deep Learning in Epileptic Seizure Detection Through EEG Signal. Proceedings of the 3rd Nanoelectronics, Circuits and Communication Systems (NCCS), Ranchi, India."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.bspc.2017.08.013","article-title":"Fuzzy distribution entropy and its application in automated seizure detection technique","volume":"38","author":"Zhang","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"101569","DOI":"10.1016\/j.bspc.2019.101569","article-title":"Epileptic seizure identification using entropy of FBSE based EEG rhythms","volume":"53","author":"Gupta","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.bbe.2018.10.006","article-title":"Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection","volume":"39","author":"Mamli","year":"2019","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.eswa.2019.03.021","article-title":"A novel approach for classification of epileptic seizures using matrix determinant","volume":"127","author":"Raghu","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.bbe.2018.03.007","article-title":"Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest","volume":"38","author":"Zhang","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Rafiuddin, N., Uzzaman Khan, Y., and Farooq, O. (2011, January 17\u201319). Feature extraction and classification of EEG for automatic seizure detection. Proceedings of the International Conference on Multimedia, Signal Processing and Communication Technologies (MSPCT 2011), Aligarh, India.","DOI":"10.1109\/MSPCT.2011.6150470"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Khan, Y.U., Rafiuddin, N., and Farooq, O. (2012, January 15\u201317). Automated seizure detection in scalp EEG using multiple wavelet scales. Proceedings of the IEEE International Conference on Signal Processing, Waknaghat Solan, India.","DOI":"10.1109\/ISPCC.2012.6224361"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Behnam, M., and Pourghassem, H. (2015, January 16\u201317). Singular Lorenz Measures Method for seizure detection using KNN-Scatter Search optimization algorithm. Proceedings of the Signal Processing and Intelligent Systems Conference (SPIS), Tehran, Iran.","DOI":"10.1109\/SPIS.2015.7422314"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TNSRE.2015.2505238","article-title":"Analysis of High-Dimensional Phase Space via Poincar\u00e9 Section for Patient-Specific Seizure Detection","volume":"24","author":"Zabihi","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"101551","DOI":"10.1016\/j.bspc.2019.04.028","article-title":"Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain","volume":"53","author":"Wei","year":"2019","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/2\/140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:08:31Z","timestamp":1760364511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/2\/140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,24]]},"references-count":93,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["e22020140"],"URL":"https:\/\/doi.org\/10.3390\/e22020140","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,24]]}}}