DocumentCode :
3582599
Title :
Normal inverse Gaussian parameters in the empirical mode decomposition domain for the detection of epilepsy and seizure
Author :
Das, Anindya Bijoy ; Ahmed, Faisal ; Bhuiyan, Mohammed Imamul Hassan
Author_Institution :
Dept. of Electr. & Electron. Eng., Bangladesh Univ. of Eng. & Technol., Dhaka, Bangladesh
fYear :
2014
Firstpage :
344
Lastpage :
349
Abstract :
In this paper, a comprehensive analysis of electroencephalogram (EEG) signals is carried out in the empirical mode decomposition (EMD) domain using a publicly available benchmark EEG database. First, the intrinsic mode functions (IMF) are extracted in the EMD domain. Next, normal inverse Gaussian (NIG) probability density function (pdf) is introduced and it is investigated whether the NIG pdf can suitably model the IMFs extracted in EMD domain of the EEG signals. It is shown that the NIG pdf is a suitable prior to model the first five IMFs extracted from various types of EEG recordings. It is further shown that the NIG parameters can distinguish among the EEG signals at the five IMF levels quite well. The analysis is further confirmed through the p-values obtained by one way ANOVA analysis. Thus, the NIG parameters in the EMD domain may be used to characterize EEG signals and help the researchers in developing fast, effective and improved classifiers for the detection of epilepsy and seizure.
Keywords :
Gaussian processes; electroencephalography; inverse problems; medical disorders; medical signal detection; probability; signal classification; statistical analysis; EEG recordings; EEG signals; EMD domain; IMF levels; NIG parameters; NIG pdf; benchmark EEG database; electroencephalogram signals; empirical mode decomposition domain; epilepsy detection; improved classifiers; intrinsic mode function; normal inverse Gaussian parameters; normal inverse Gaussian probability density function; one way ANOVA analysis; p-values; seizure detection; Brain modeling; Computers; Databases; Electroencephalography; Empirical mode decomposition; Information technology; Probability density function; Electroencephalogram(EEG); Empirical Mode Decomposition(EMD); Epileptic seizure; Normal Inverse Gaussian(NIG);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Information Technology (ICCIT), 2014 17th International Conference on
Type :
conf
DOI :
10.1109/ICCITechn.2014.7073073
Filename :
7073073
Link To Document :
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