Open AccessEpileptic Detection from the Eeg Signal using the Anti colony Optimization Technique with Deep Neural Network
Author(s) -
Ruchi Sharma,
Khyati Chopra
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a2314.059120
Subject(s) - electroencephalography , pattern recognition (psychology) , ictal , computer science , artificial intelligence , artificial neural network , ant colony optimization algorithms , signal (programming language) , entropy (arrow of time) , filter (signal processing) , speech recognition , computer vision , psychology , physics , quantum mechanics , psychiatry , programming language
Extensively used technique to diagnose the epilepsy is EEG. The research objective is to check the variations of frequency found in the epileptic EEG signals.. The EEG dataset were acquired from online database of the Bonn University (BU). Then, butterworth type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, ant colony optimization technique was proposed for optimizing the extracted features. The optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). The accuracy attained using the ant colony with deep neural network is 98.12% using the BU EEG dataset, respectively related to the existing models.