{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:15:00Z","timestamp":1772554500386,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh\u2013Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.<\/jats:p>","DOI":"10.3390\/s20174952","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T14:21:10Z","timestamp":1598970070000},"page":"4952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Prasanna","family":"J.","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India"}]},{"given":"M. S. P.","family":"Subathra","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9030-8102","authenticated-orcid":false,"given":"Mazin Abed","family":"Mohammed","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-5430","authenticated-orcid":false,"given":"Mashael S.","family":"Maashi","sequence":"additional","affiliation":[{"name":"Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9356-1186","authenticated-orcid":false,"given":"Begonya","family":"Garcia-Zapirain","sequence":"additional","affiliation":[{"name":"Evida Lab, University of Deusto, Avda\/Univesidades 24, 48007 Bilbao, Spain"}]},{"given":"N. J.","family":"Sairamya","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0304-495X","authenticated-orcid":false,"given":"S. Thomas","family":"George","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,1]]},"reference":[{"key":"ref_1","unstructured":"Chiang, C., Chang, N., Chen, T., Chen, H., and Chen, L. (September, January 30). Seizure Prediction Based on Classiffication of EEG Synchronization Patterns with On-line Retraining and Post-Processing Scheme. Proceedings of the 33rd Annual International Conference of the IEEE EMBS, Boston, MA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.cmpb.2017.03.002","article-title":"Predicting epileptic seizures from scalp EEG based on attractor state analysis","volume":"143","author":"Chu","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Park, C., Choi, G., Kim, J., Kim, S., Kim, T., Min, K., Jung, K., and Chong, J. (2018, January 24\u201327). Epileptic Seizure Detection for Multi-channel EEG with Deep Convolutional Neural Network. Proceedings of the 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, USA.","DOI":"10.23919\/ELINFOCOM.2018.8330671"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shahid, A., Kamel, N., Malik, A.S., and Jatoi, M.A. (2013, January 25\u201328). Epileptic seizure detection using the singular values of EEG signals. Proceedings of the 2013 International Conference on Complex Medical Engineering (ICME), Beijing, China.","DOI":"10.1109\/ICCME.2013.6548330"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2013.11.010","article-title":"A framework on wavelet-based non-linear features and extreme learning machine for epileptic seizure detection","volume":"10","author":"Chen","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s00521-018-3882-6","article-title":"Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques","volume":"32","author":"Mohammed","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"160","DOI":"10.14419\/ijet.v7i4.36.23737","article-title":"Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer","volume":"7","author":"Obaid","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mutlag, A.A., Khanapi Abd Ghani, M., Mohammed, M.A., Maashi, M.S., Mohd, O., Mostafa, S.A., Abdulkareem, K.H., Marques, G., and de la Torre D\u00edez, I. (2020). MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management. Sensors, 20.","DOI":"10.3390\/s20071853"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.compeleceng.2018.07.044","article-title":"Trainable model for segmenting and identifying Nasopharyngeal carcinoma","volume":"71","author":"Mohammed","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"99115","DOI":"10.1109\/ACCESS.2020.2995597","article-title":"Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods","volume":"8","author":"Mohammed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.patrec.2017.03.017","article-title":"Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform","volume":"94","author":"Gupta","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.patrec.2017.05.007","article-title":"Classification of focal and non focal EEG using entropies","volume":"94","author":"Arunkumar","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.knosys.2016.11.024","article-title":"Systems An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter bank","volume":"118","author":"Sharma","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.eswa.2018.06.031","article-title":"Classification of focal and Non-focal EEG signals using neighborhood component analysis and machine learning algorithms","volume":"113","author":"Raghu","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.bspc.2016.05.004","article-title":"Discrimination and classification of NFC and FC EEG signals using entropy-based features in the EMD-DWT domain","volume":"29","author":"Das","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jocs.2017.03.022","article-title":"Decision support system for focal EEG signals using tunable-Q wavelet transform","volume":"20","author":"Sharma","year":"2017","journal-title":"J. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13521","DOI":"10.1007\/s10586-018-1995-4","article-title":"Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition","volume":"22","author":"Kumar","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, A., Pachori, R.B., and Acharya, U.R. (2017). Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis. Entropy, 19.","DOI":"10.3390\/e19030099"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.jocs.2018.02.002","article-title":"Entropy features for focal EEG and non focal EEG","volume":"27","author":"Arunkumar","year":"2018","journal-title":"J. Comput. Sci."},{"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":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"669","DOI":"10.3390\/e17020669","article-title":"Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals","volume":"17","author":"Sharma","year":"2015","journal-title":"Entropy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/TNSRE.2016.2604393","article-title":"Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG","volume":"25","author":"Chen","year":"2016","journal-title":"Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_24","first-page":"160","article-title":"Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier","volume":"41","author":"Sriraam","year":"2017","journal-title":"Image Signal Process."},{"key":"ref_25","first-page":"31","article-title":"Epileptogenic Focus Detection in Intracranial EEG based on Delay Permutation Entropy","volume":"1559","author":"Zhu","year":"2013","journal-title":"Int. Symp. Comput. Models Life Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1002\/ima.22199","article-title":"Classification of Focal and Nonfocal EEG Signals Using ANFIS Classifier for Epilepsy Detection","volume":"26","author":"Deivasigamani","year":"2016","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1740002","DOI":"10.1142\/S0219519417400024","article-title":"Classification of focal and non-focal EEG signals using features derived from fourier based rhythms","volume":"17","author":"Singh","year":"2017","journal-title":"J. Mech. Med. Biol."},{"key":"ref_28","first-page":"16073","article-title":"Feature Extraction and Selection of a Combination of Entropy Features for Real-time Epilepsy Detection","volume":"5","author":"Abhinaya","year":"2016","journal-title":"Int. J. Eng. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/978-981-13-0923-6_50","article-title":"Automated Identification system for Focal EEG Signals Using Fractal Dimension of FAWT-Based Sub-bands Signals","volume":"Volume 748","author":"Dalal","year":"2019","journal-title":"Machine Intelligence and Signal Analysis"},{"key":"ref_30","first-page":"687","article-title":"Computer Aided Automatic Detection and Classification of EEG Signals for Screening Epilepsy Disorder","volume":"34","author":"Deivasigamani","year":"2018","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_31","first-page":"614","article-title":"Genetic Algorithm based Feature Selection for Classification of NFC and FC Intracranial Electroencephalographic Signals","volume":"76","author":"Sathish","year":"2017","journal-title":"J. Sci. Ind. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"325","DOI":"10.3389\/fphys.2018.00325","article-title":"Automatic Change Detection for Real-Time Monitoring of EEG Signals","volume":"9","author":"Gao","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fasil, O.K., Rajesh, R., and Thasleema, T.M. (2017, January 21\u201323). Influence of differential features in non-focal and focal EEG signal classification. Proceedings of the 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh.","DOI":"10.1109\/R10-HTC.2017.8289042"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Itakura, T., and Tanaka, T. (2017, January 12\u201315). Epileptic Focus Localization Based on Bivariate Empirical Mode Decomposition and Entropy. Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282255"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bashar, M.K., Reza, F., Idris, Z., and Yoshida, H. (2016, January 4\u20138). Epileptic seizure classification from intracranical EEG signals: A comparative study EEG-based seizure classification. Proceedings of the 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES.2016.7843422"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"77255","DOI":"10.1109\/ACCESS.2020.2989442","article-title":"Classification of non-focal and focal Epileptic patients using single channel EEG and Long short-term memory learning system","volume":"8","author":"Fraiwan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/JSEN.2019.2956072","article-title":"Time-frequency domain deep convolutional neural network and non-focal EEG signals","volume":"20","author":"Srirangan","year":"2020","journal-title":"IEEE Sensors"},{"key":"ref_38","unstructured":"Poomipat, B., Apiwat, L., and Jitkomut, S. (2020, January 4\u20138). Automatic epileptic seizure onset-offset detection based on CNN in scalp EEG. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"65880","DOI":"10.1109\/ACCESS.2020.2983917","article-title":"Epileptic seizure detection with a reduced montage: A way forward for Ambulatory EEG devices","volume":"8","author":"Raheel","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6542","DOI":"10.1109\/JSEN.2020.2976519","article-title":"EEG-Rhythm specific Taylor-Fourier bank implemented with O-splines for the detection of epilepsy using EEG signals","volume":"20","author":"Jose","year":"2020","journal-title":"IEEE Sensors"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101761","DOI":"10.1016\/j.bspc.2019.101761","article-title":"Automatic focal and non-focal EEG detection using entropy based features from flexible analytic wavelet transform","volume":"57","author":"Yang","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.bspc.2019.01.012","article-title":"Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking","volume":"50","author":"Mohammed","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s10462-019-09698-4","article-title":"Classification of focal and non-focal EEG signals using empirical mode decomposition (EMD), phase space reconstruction and neural networks","volume":"52","author":"Wei","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101856","DOI":"10.1016\/j.bspc.2020.101856","article-title":"Automated focal EEG signal detection based on third order cumulant function","volume":"58","author":"Raghu","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Andrzejak, R.G., Schindler, K., and Rummel, C. (2012). Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86.","DOI":"10.1103\/PhysRevE.86.046206"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C.E. (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.","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Saka, K., Aydemir, \u00d6., and \u00d6zt\u00fcrk, M. (2016, January 27\u201329). Classification of EEG Signals Recorded During Right\/Left Hand Movement Imagery Using Fast Walsh Hadamard Transform Based Features. Proceedings of the 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Vienna, Austria.","DOI":"10.1109\/TSP.2016.7760909"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s10916-016-0579-1","article-title":"An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks","volume":"40","author":"Sareen","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_49","first-page":"56","article-title":"A Cloud-Based Seizure Alert System for Epileptic Patients That Uses Higher-Order Statistics","volume":"18","author":"Sareen","year":"2016","journal-title":"Cloud Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s10916-015-0323-2","article-title":"Human Identification Using Compressed ECG Signals","volume":"39","author":"Camara","year":"2015","journal-title":"J. Med. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1049\/ip-vis:20010674","article-title":"Novel FPGA implementations of Walsh-Hadamard transforms for signal processing","volume":"148","author":"Amira","year":"2001","journal-title":"IEEE Proc. Image Signal Process."},{"key":"ref_52","unstructured":"Deveci, T.C., Cakir, S., and Cetin, A.E. (2018). Energy Efficient Hadamard Neural Networks. arXiv, 1\u201315."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1049\/iet-ipr.2018.5418","article-title":"Classification of EEG signals for detection of epileptic seizure activities based on feature extraction from brain maps using image processing algorithms","volume":"12","author":"Jothiraj","year":"2018","journal-title":"IET Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2015.08.004","article-title":"Application of entropies for automated diagnosis of epilepsy using EEG signals: A review","volume":"88","author":"Acharya","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.compbiomed.2017.05.028","article-title":"Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics","volume":"87","author":"Jord","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"046217","DOI":"10.1103\/PhysRevE.70.046217","article-title":"Detecting dynamical changes in time series using the permutation entropy","volume":"70","author":"Cao","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TNSRE.2007.897025","article-title":"Characterization of Surface EMG Signal Based on Fuzzy Entropy","volume":"15","author":"Chen","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1016\/j.asoc.2010.11.020","article-title":"Complexity analysis of the biomedical signal using fuzzy entropy measurement","volume":"11","author":"Xie","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jneumeth.2015.01.015","article-title":"The detection of epileptic seizure signals based on fuzzy entropy","volume":"243","author":"Xiang","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1007\/s10439-009-9795-x","article-title":"Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure","volume":"37","author":"Serap","year":"2009","journal-title":"Ann. Biomed. Eng."},{"key":"ref_62","first-page":"1811","article-title":"Classification Using ANN: A Review","volume":"13","author":"Bala","year":"2017","journal-title":"Int. J. Comput. Intell. Res."},{"key":"ref_63","first-page":"57","article-title":"Neural network classification of EEG signals by using AR with MLE processing for epleptic seizure detection","volume":"10","author":"Subasi","year":"2005","journal-title":"Math. Comput. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kumar, N., and Malik, H. (2018). Automatic Classification for Neural Signals in Epilepsy Using Artificial Neural Network. EasyChair Prepr.","DOI":"10.29007\/lfrr"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"31","DOI":"10.9790\/0661-16123135","article-title":"Comparison of Neural Network Training Functions for Hematoma Classification in Brain CT Images","volume":"16","author":"Sharma","year":"2014","journal-title":"IOSR J. Comput. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"10054","DOI":"10.1016\/j.eswa.2009.01.022","article-title":"Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines","volume":"36","author":"Lima","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"9072","DOI":"10.1016\/j.eswa.2012.02.040","article-title":"Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework","volume":"39","author":"Acharya","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.dsp.2008.07.004","article-title":"Combined neural network model employing wavelet coefficients for EEG signals classification","volume":"19","year":"2009","journal-title":"Digit. Signal Process."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1049\/htl.2018.5036","article-title":"Detection of focal electroencephalogram signals using higher-order moments in EMD-TKEO domain","volume":"6","author":"Chatterjee","year":"2019","journal-title":"Healthc. Technol. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neulet.2018.10.062","article-title":"Time-domain exponential energy for epileptic EEG signal classification","volume":"694","author":"Fasil","year":"2019","journal-title":"Neurosci. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"20080","DOI":"10.1109\/ACCESS.2020.2969055","article-title":"A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gupta, V., and Pachori, R.B. (2020). Classification of focal EEG signals using FBSE based flexible time-frequency coverage wavelet transform. Biomed. Signal Process. Control, in press.","DOI":"10.1016\/j.bspc.2020.102124"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:05:39Z","timestamp":1760177139000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,1]]},"references-count":72,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174952"],"URL":"https:\/\/doi.org\/10.3390\/s20174952","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,1]]}}}