Epileptic Seizure Prediction Using Hybrid Feature Selection
نویسندگان
چکیده
A comprehensive research of Electroencephalography (EEG) is carried out on Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) domains. In this scenario, the hybrid feature extraction is performed by utilizing entropy features like Shannon entropy, log-energy entropy and Renyi entropy. Generally, the entropy measures are effective in evaluation of non-linear interrelation and complexity of signals. After that, a superior classifier named as Support Vector Machine (SVM) is implemented for classifying the signals. Experimental outcome proves that the advanced method distinguishes the focal and non-focal signals with a superior accuracy.
منابع مشابه
Efficient Feature Selection Using a Hybrid Algorithm for the Task of Epileptic Seizure Detection
Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection t...
متن کاملFeature selection in high dimensional EEG features spaces for epileptic seizure prediction
Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy; seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional feature space created by a variety of ...
متن کاملEpileptic seizure detection based on The Limited Penetrable visibility graph algorithm and graph properties
Introduction: Epileptic seizure detection is a key step for both researchers and epilepsy specialists for epilepsy assessment due to the non-stationariness and chaos in the electroencephalogram (EEG) signals. Current research is directed toward the development of an efficient method for epilepsy or seizure detection based the limited penetrable visibility graph (LPVG) algorith...
متن کاملOptimal preictal period in seizure prediction
A statistical method for finding the optimal preictal period to be used in epileptic seizure prediction algorithms is presented. As supervised machine learning methods need labeled training samples, the adequate selection of preictal period plays a key role in the training of an efficient classifier employed in seizure prediction. The proposed method uses amplitude distribution histograms of a ...
متن کاملA Hybrid Approach Based on Higher Order Spectra for Clinical Recognition of Seizure and Epilepsy Using Brain Activity
Introduction: This paper proposes a reliable and efficient technique to recognize different epilepsy states, including healthy, interictal, and ictal states, using Electroencephalogram (EEG) signals. Methods: The proposed approach consists of pre-processing, feature extraction by higher order spectra, feature normalization, feature selection by genetic algorithm and ranking method, and classif...
متن کامل