AN EFFICIENT EPILEPTIC SEIZURE DETECTION USING ENTROPY FEATURES WITH OPTIMAL NEURAL NETWORK
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 3, Pages 5622-5638
AbstractThe planning of electroencephalogram (EEG) signal location is a difficult errand because of the need of removing delegate designs from multidimensional time arrangement created from EEG estimations. Productively recognizing epileptic seizure EEG signals is helpful in taking care of neurological variations from the norm and furthermore in assessment of the physiological condition of the mind for a wide scope of utilizations in the field of biomedical. The electrical activity of the brain is indicated by the EEG signals and also it contains useful information about the state of the brain for studying brain function. In the manual scoring there is always a chance for human errors, also it consumes a lot of time, process is costly and not sufficient enough for reliable information. This developed a need of designing an automatic system for evaluating and diagnosing epileptic seizure EEG signals to eliminate the chance of the analyst missing data. An Adaptive artificial neural network (AANN) is used in the proposed approach to detect normal or epileptic signal. Also, oppositional crow search algorithm (OCSA) is used for optimal designing of the epileptic seizure detection system. The proposed method is to be implemented using Matlab software. The simulation results are to be compared with existing approaches to calculate its effectiveness.
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