Detecting Epileptic Seizures Using Electroencephalogram: A New and Optimized Method for Seizure Classification Using Hybrid Extreme Learning Machine

Epilepsy is one of the frequent brain disorder that may consequence in the brain dysfunction and cognitive disorders. Epileptic seizures can occur due to transient and unexpected electrical interruptions of brain. EEG (ElectroEncephaloGram) is one of the non-invasive methods for analyzing the human brain dynamics that affords a direct evaluation of cortical behavior. Seizures are featured by short and episodic neuronal synchronous discharges with considerably enlarged amplitude. This uneven synchrony may happen in the brain accordingly i.e., partial seizures visible only in few channels of the EEG signal or generalized seizures, which are seen in every channel of the EEG signal involving the whole brain. Existing analysis of epilepsy depends on difficult visual screening by extremely trained clinicians. Data recordings create very lengthy data, and therefore the inspection and identification of epilepsy takes more time for diagnosis. At present time computerized systems are usually established to make the diagnosis simpler. This paper discusses an implementation of automated epileptic EEG detection system using neural networks. In this paper, a statistical parameter regarded as Sample Entropy (SampEn), is used as a method for feature extraction for performing the task of classifying EEG signals, which are normal, ictal and interictal. Here the value of SampleEn plays a vital role in the diagnosis of Epileptic seizures. It is been observed that the value of SampEn falls suddenly during an Epileptic Seizure, and hence this information delineates the exact occurrence of Epilepsy. Classification strategies are based on models like Back-propagation Neural Network (BPNN),Support Vector Machine(SVM) and Extreme Learning Machine (ELM). Hybrid ELM (HELM) is the classification method devised in the current paper and is the first of its kind to be implemented for EEG classification. This classification method uses the Analytical Hierarchy Process (AHP) method to select the inp- - ut weights and hidden biases, the ELM algorithm to analytically determine the output weights and the Levenberg-Marquardt (LM) algorithm to learn the network. Experimental results show that automatic epilepsy detection using sample entropy (SampEn) and Hybrid ELM achieves better accuracy in lesser time.