<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG (Electroencephalogram) signals of twenty patients are collected from Sri Ramakrishna Hospitals at Coimbatore. The raw EEG signals are sampled and various parameters like energy, energy, variance, peaks, sharp and spike waves, duration, events and covariance. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy by converting the EEG signal parameters in to code patterns by fuzzy systems. Elman-Chaotic optimization is identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. This classification provides a better way of treating the epileptic patients. This project aims to safeguard a patient’s life when critical situation occurs. Future scope is to design an embedded system which collects the raw EEG signals from the brain and directly gives the level of epilepsy. It will make the neural surgeons to give appropriate remedial measures.</p></abstract>

This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG (Electroencephalogram) signals of twenty patients are collected from Sri Ramakrishna Hospitals at Coimbatore. The raw EEG signals are sampled and various parameters like energy, energy, variance, peaks, sharp and spike waves, duration, events and covariance. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy by converting the EEG signal parameters in to code patterns by fuzzy systems. Elman-Chaotic optimization is identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. This classification provides a better way of treating the epileptic patients. This project aims to safeguard a patient’s life when critical situation occurs. Future scope is to design an embedded system which collects the raw EEG signals from the brain and directly gives the level of epilepsy. It will make the neural surgeons to give appropriate remedial measures.

Performance Analysis of Patient Specific Elman-Chaotic Optimization Model for Fuzzy Based Epilepsy Risk Level Classification from Eeg Signals

Bannari Amman Institute of Technology Sathyamangalam

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG...

Performance Analysis of Patient Specific Elman-Chaotic Optimization Model for Fuzzy Based Epilepsy Risk Level Classification from Eeg Signals

Publicado en línea: 03 nov 2017

Páginas: 612 - 635

DOI: https://doi.org/10.21307/ijssis-2017-371

Palabras clave
EEG Signals, Epilepsy risk levels, Fuzzy Logic, Chaotic Optimization, Elman Neural Network

© 2009 R. HariKumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Performance Analysis of Patient Specific Elman-Chaotic Optimization Model for Fuzzy Based Epilepsy Risk Level Classification from Eeg Signals

Publicado en línea: 03 nov 2017

Páginas: 612 - 635

DOI: https://doi.org/10.21307/ijssis-2017-371

Palabras claveEEG Signals, Epilepsy risk levels, Fuzzy Logic, Chaotic Optimization, Elman Neural Network

© 2009 R. HariKumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
EEG Signals, Epilepsy risk levels, Fuzzy Logic, Chaotic Optimization, Elman Neural Network