Extreme Learning Machine (ELM) based Performance Analysis and Epilepsy Identification from EEG Signals

Abstract

Epilepsy is a chronic brain condition affecting one in 100 patients. EEG (Electroencephalogram) is a signal representing the influence of combining various processes in the brain. This paper aims to identify the likelihood levels of epilepsy from the features extracted from the EEG signals and to compare them with the output of the Singular Value Decomposition and Extreme Learning Machine (ELM). The SVD approach extracts functionality from EEG signals (that is, to minimize dimensionality) while the ELM is used as a classification system. A study of twenty patients is documented in this paper. The criteria such as the Performance Index (PI), Sensitivity (Se), Specificity (Sp), Average Detection (AD), and Good Detection Ratio (GDR) are evaluated using ELM to identify epilepsy. Results show that when SVD is classified with ELM at sigmoid activation function, an average detection of 98.94% and a GDR of 97.83% are obtained.