Multi-feature characterization of epileptic activity for construction of an automated internet-based annotated classification.
Continuous monitoring of EEG is essential for the neurologist to detect the epileptic seizures that occur at various intervals. Since large volume of data need to be analyzed, visual analysis has been proven to be time consuming and subsequently automated detection techniques have gained importance in the recent years. For the biomedical research community, the major challenge lies in providing a solution to neurologists in terms of diagnosis and EEG database management. This paper discusses the automated detection of epileptic seizure using frequency domain and entropy parameters which helps in the construction of epileptic database for handling EEG data. Experimental study indicates that the suggested mode of operation can be used for internet based framework which contains pure epileptic patterns in the server. This can be retrieved and analyzed for detection and annotation of epileptic spikes in extensive EEG recordings.
Arvind
R
R
Department of Biomedical Engineering, SSN College of Engineering, Chennai, India.
Karthik
B
B
Sriraam
Natarajan
N
D000465:Algorithms; D004569:Electroencephalography; D001330:Electronic Data Processing; D019277:Entropy; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D020407:Internet; D008991:Monitoring, Physiologic / Q000379:methods*; D014584:User-Computer Interface
Electroencephalogr Clin Neurophysiol. 1991 Jul;79(1):11-9
1713547
Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Dec;64(6 Pt 1):061907
11736210
Neurol Res. 2004 Jan;26(1):55-60
14977058
IEEE Trans Inf Technol Biomed. 2007 May;11(3):288-95
17521078
Conf Proc IEEE Eng Med Biol Soc. 2004;2004:3481-4
17271036
J Med Syst. 2005 Dec;29(6):647-60
16235818
Electroencephalogr Clin Neurophysiol. 1974 Nov;37(5):538-48
4138729
Comput Methods Programs Biomed. 2008 Aug;91(2):100-9
18406490
Comput Methods Programs Biomed. 2005 Dec;80(3):187-94
16219385