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EMD-Chaos based analysis of EEG signals for early seizure detection


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  • Product Description

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.

Product Specifications
SKU :COC82300
AuthorN. F. N. Aurangozeb,Tarek Shahriar and H. M. Shahriar Hassan
Number of Pages124
Publishing Year2011-12-08T00:00:00.000
Edition1 st
Book TypeElectronics & communications engineering
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-10-08 00:00:00