Feature extraction of linear predictors at spectral bands of interest.
Intra-cranial electroencephalograms (EEG) from two patients diagnosed with epilepsy are sampled at 1 kHz, enabling analysis and feature extraction at frequency bands above the gamma range. This study focuses on the extraction of linear features (including autoregressive, autoregressive-moving average and Fourier coefficients) obtained at both low (below 100 Hz) and high (100-500 Hz) bands of the signal spectrum. Comparisons of the performance of each feature are made based on a binary hypothesis test of statistical distributions from inter-ictal and pre-ictal epochs. Results are obtained from pre-ictal time periods as assessed by an expert epileptologist.
Leondopulos
Stathis S
SS
Rutgers University, NJ 08854, USA. stathis@ece.rutgers.edu
Chaovalitwongse
Wanpracha A
WA
Micheli-Tzanakou
Evangelia
E
Wong
Stephen
S
Brenda
Y U
YU
D000465:Algorithms; D001699:Biometry; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D005583:Fourier Analysis; D006624:Hippocampus / Q000473:pathology; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors