Title :
Automatic detection of epileptic EEG using THFB and auroregressive modeling
Author :
Khatavkar, S.S. ; Gawande, J.P.
Author_Institution :
Dept. of Instrum. & Control, Cummins Coll. of Eng. for Women, Pune, India
Abstract :
In this work, the EEG signal is decomposed into its five subbands viz. delta (0.8-4Hz), theta (4-8Hz), alpha (8-15Hz), beta (15-30Hz), gamma (above 30Hz) using Triplet Half-band Filter Bank (THFB). Then, the autoregressive (AR) model is computed for each subband. Next, power spectral density (PSD) of the AR coefficients of each subbands is estimated for classfication of normal and epileptic EEG. It is observed that classification performed using THFB-AR modeling method gives better classification accuracy than existing method (approximate entropy).
Keywords :
autoregressive processes; channel bank filters; electroencephalography; medical signal detection; AR coefficients; THFB-AR modeling method; automatic detection; autoregressive modeling; epileptic EEG signal; power spectral density; triplet half-band filter bank; Accuracy; Brain models; Computational modeling; Electroencephalography; Mathematical model; Wavelet transforms; Autoregressive model; Electroencephalogram; Power Spectral Density (PSD); Seizure detection; Wavelet filter bank;
Conference_Titel :
India Conference (INDICON), 2014 Annual IEEE
Conference_Location :
Pune
Print_ISBN :
978-1-4799-5362-2
DOI :
10.1109/INDICON.2014.7030585
