Feature extraction of linear predictors at spectral bands of interest

Author

Leondopulos, Stathis S. ; Chaovalitwongse, Wanpracha A. ; Micheli-Tzanakou, Evangelia ; Wong, Stephen ; Brenda, Y.W.

Author_Institution

Rutgers Univ., New Brunswick, NJ, USA

fYear

2009

fDate

3-6 Sept. 2009

Firstpage

2612

Lastpage

2616

Abstract

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.

Keywords

electroencephalography; medical image processing; statistical distributions; Fourier coefficients; binary hypothesis test; epileptologist; feature extraction; frequency 1 kHz; inter-ictal epoch; intra-cranial electroencephalograms; linear predictors; patient diagnosis; pre-ictal epoch; signal spectrum; spectral bands; statistical distribution; Algorithms; Biometry; Electroencephalography; Epilepsy; Fourier Analysis; Hippocampus; Humans; Linear Models; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Signal Processing, Computer-Assisted; Software; Time Factors;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Conference_Location

Minneapolis, MN

ISSN

1557-170X

Print_ISBN

978-1-4244-3296-7

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2009.5335397

Filename

5335397