Fuzzy-Based Automatic Epileptic Seizure Detection Framework
作者机构：Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabadPakistan Department of Computer Science&ITUniversity of Lakki MarwatKPKPakistan Stockholm UniversityStockholmSweden Department of Computer ScienceSchool of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of SoftwareKorea National University of TransportationChungju27469Korea Department of Biomedical EngineeringKorea National University of TransportationChungju27469Korea Department of AI Robotics EngineeringKorea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR)Korea National University of TransportationChungju27469Korea
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第70卷第3期
页 面：5601-5630页
核心收录：
基 金：This work was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant No.NRF-2020R1I1A3074141) the Brain Research Program through the NRF funded by the Ministry of Science,ICT and Future Planning(Grant No.NRF-2019M3C7A1020406),and“Regional Innovation Strategy(RIS)”through the NRF funded by the Ministry of Education
主 题：Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization
摘 要：Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.
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