E-health design with spectral analysis, linear layer neural networks and adaboost classifier for epilepsy classification from EEG signals

Abstract

About 1–2% of the population in the whole world is suffering from a serious neurological disorder called epilepsy which is characterized by spontaneous seizures. A lot of temporary disruptions occur in the ongoing electrical activities of the brain if the seizure attack is present. Antiepileptic drugs may be favourable for some patients while for other patients it may not respond well. To explore the electrical behaviour of the human brain, the measurement and the recordings of the electrical brain activity is done. By analyzing the Electroencephalography (EEG) signals and extracting all its features including both univariate and multivariate, various algorithms for seizure prediction, detection, classification have been developed. In this paper, an e-health design for epilepsy classification with the help of spectral analysis, Linear Layer Neural Networks (LLNN) and Adaboost Classifier has been proposed. The LLNN has been used as the preliminary level classifier and as the results obtained through it are not satisfactory, further optimization and classification is done with the help of Adaboost Classifier. Results show that when classified with Adaboost Classifier an average classification accuracy of about 99.43%, an average quality value of 24.38, an average less time delay of 1.99 s along with an average performance index of 99.13% is obtained.