Detection and classification of fast ripples using wavelets

Author

Kachenoura, Amar ; Birot, Gwenael ; Albera, Laurent ; Senhadji, Lotfi ; Wendling, Fabrice

Author_Institution

INSERM, Rennes, France

fYear

2013

fDate

11-13 Sept. 2013

Firstpage

81

Lastpage

84

Abstract

Fast ripples (FRs) are hypothesized to be a biomarker of epileptogenic processes. In this communication, we introduce a two-step procedure for automatically detecting and classifying FRs. In the first step, we detect all events of interest (EOIs) in the frequency band ranging from 250 Hz to 600 Hz. Then, based on wavelet transform, a local energy vs frequency analysis is performed to assign each EOIs to a specific class: FRs, interictal epileptic spikes (IESs), and artifact. The results obtained in the context of real depth-EEG signals (human and animal) show high performance in term of sensitivity and specificity.

Keywords

electroencephalography; medical disorders; medical signal processing; wavelet transforms; epileptogenic process; fast ripple classification; fast ripple detection; frequency 250 Hz to 600 Hz; frequency band; interictal epileptic spike; real depth-EEG signal; wavelet transform; Animals; Hippocampus; IIR filters; Neuroscience; Transient analysis; Wavelet transforms; EEG; classification; detection; epilepsy; fast ripple; interictal epileptic spikes; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Biomedical Engineering (ICABME), 2013 2nd International Conference on

Conference_Location

Tripoli

Print_ISBN

978-1-4799-0249-1

Type

conf

DOI

10.1109/ICABME.2013.6648852

Filename

6648852