RESEARCH ARTICLE


A Robustness Comparison of Two Algorithms Used for EEG Spike Detection



Sahbi Chaibi 1, *, Tarek Lajnef 1, Abdelbacet Ghrob 1, Mounir Samet 1, Abdennaceur Kachouri 1, 2
1 National Engineering School of Sfax, LETI Laboratory, ENIS BPW3038-Sfax, Tunisia
2 ISSIG: Higher Institute of Industrial Systems, Gabes CP 6011, Tunisia


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Creative Commons License
© Chaibi et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the National Engineering School of Sfax, LETI Laboratory, ENIS BPW3038-Sfax, Tunisia; Tel: +216 25 621 084; E-mail: sahbi.chaibi@yahoo.fr


Abstract

Spikes and sharp waves recorded on scalp EEG may play an important role in identifying the epileptogenic network as well as in understanding the central nervous system. Therefore, several automatic and semi-automatic methods have been implemented to detect these two neural transients. A consistent gold standard associated with a high degree of agreement among neuroscientists is required to measure relevant performance of different methods. In fact, scalp EEG data can often be corrupted by a set of artifacts and are not always served as data of gold standard. For this reason, the use of intracerebral EEG data mixed with gaussian noise seems to best resemble the output of scalp EEG brain and serves as a consistent gold standard. In the present framework, we test the robustness of two important methods that have been previously used for the automatic detection of epileptiform transients (spikes and sharp waves). These methods are based respectively on Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). Our purpose is to elaborate a comparative study in terms of sensitivity and selectivity changes via the decrease of Signal to Noise Ratio (SNR), which is ranged from 10 dB up to -10 dB. The results demonstrate that, DWT approach turns to be more stable in terms of sensitivity, and it successfully follows the detection of relevant spikes with the decrease of SNR. However, CWT-based approach remains more stable in terms of selectivity, so that, it performs well in terms of rejecting false spikes compared to DWT approach.

Keywords: CWT, DWT, Epilepsy, noisy neural data, Stereo-Electroencephalography (SEEG).