EMD-Chaos based analysis of EEG signals for early seizure detection - Aurangozeb, N. F. N.;Shahriar, Tarek;Hassan, H. M. Shahriar
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In this thesis, a method has been developed to analyze EEG signals for early detection of seizure using empirical mode decomposition (EMD) and chaos.Chaos in EEG is de ned by the tendency to gravitate towards speci c regions in phase space. Lyapunov exponent and Kol-mogorov complexity are the important factors regarding chaotic behavior of any dynamical system. In this thesis, the Largest Lyapunov Exponent (LLE) of the EEG signal over time is observed and decision about Epileptic Seizure is taken. It is seen that from normal to seizure state transition, the amount of chaos in EEG is…mehr

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Produktbeschreibung
In this thesis, a method has been developed to analyze EEG signals for early detection of seizure using empirical mode decomposition (EMD) and chaos.Chaos in EEG is de ned by the tendency to gravitate towards speci c regions in phase space. Lyapunov exponent and Kol-mogorov complexity are the important factors regarding chaotic behavior of any dynamical system. In this thesis, the Largest Lyapunov Exponent (LLE) of the EEG signal over time is observed and decision about Epileptic Seizure is taken. It is seen that from normal to seizure state transition, the amount of chaos in EEG is drastically reduced. Thus, the behavior of chaos in EEG signal described above can be used for seizure detection.
Autorenporträt
Aurangozeb received his BS degree in Electrical and Electronic Engineering (EEE) from the Bangladesh University of Engineering and Technology (BUET) in 2009.