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An optimized design of seizure detection system using joint feature extraction of multichannel EEG signals

The Journal of Biomedical Research 2020年第3期34卷 191-204页

作者： Dattaprasad Torse Veena Desai Rajashri Khanai Department of Electronics and Communication Engineering KLS Gogte Institute of TechnologyBelagaviKarnataka 590008India Department of Electronics and Communication Engineering KLE Dr.M.S.Sheshgiri College of Engineering and TechnologyBelagaviKarnataka 590008India

The detection of seizure onset and events using electroencephalogram(EEG) signals are important tasks in epilepsy *** literature available on seizure detection has discussed the implementation of advanced signal processing algorithms using tools accessed over the ***,seizure monitoring application needs near sensor processing due to privacy and latency *** this paper,a real time seizure detection system has been implemented using an embedded *** proposed system is based on ensemble empirical mode decomposition(EEMD) and tunable-q wavelet transform(TqWT) *** analysis and classification of non-stationary EEG signals require the wavelet transform with high ***,direct use of TqWT increases the computational complexity of feature extraction from multivariate EEG *** this paper,the first step is to process the signal by using EEMD to obtain 8 intrinsic mode functions(IMFs).The Kraskov(KraEn),sample(SampEn),and permutation(PermEn) entropy features of IMFs are extracted and based on optimum values,and 4 IMFs are decomposed using ***,centered correntropy(CenCorrEn) features of the 1^(st)and 16^(th) sub-band of TqWT have been used as classifier *** performance of multilayer perceptron neural networks(MLPNN),least squares support vector machine(LSSVM),and random forest(RF) classifiers has been tested on the multichannel EEG data recorded from a local *** RF classifier has produced the highest accuracy of 96.2% in classifying the *** proposed scheme has been employed in developing an embedded seizure detection system to assist neurologists in making seizure diagnostic decisions.

关键词： seizure detection electroencephalogram ensemble empirical mode decomposition intrinsic mode function tunable-q wavelet transform

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