title: A Robustness Comparison of Two Algorithms Used for EEG Spike Detection.
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.
authors: National Engineering School of Sfax, LETI Laboratory, ENIS BPW3038-Sfax, Tunisia.
authors: Sahbi
authors: S
authors: Chaibi
authors: National Engineering School of Sfax, LETI Laboratory, ENIS BPW3038-Sfax, Tunisia.
authors: Tarek
authors: T
authors: Lajnef
authors: National Engineering School of Sfax, LETI Laboratory, ENIS BPW3038-Sfax, Tunisia.
authors: Abdelbacet
authors: A
authors: Ghrob
authors: National Engineering School of Sfax, LETI Laboratory, ENIS BPW3038-Sfax, Tunisia.
authors: Mounir
authors: M
authors: Samet
authors: National Engineering School of Sfax, LETI Laboratory, ENIS BPW3038-Sfax, Tunisia ; ISSIG: Higher Institute of Industrial Systems, Gabes CP 6011, Tunisia.
authors: Abdennaceur
authors: A
authors: Kachouri
keywords: CWT; DWT; Epilepsy; Stereo-Electroencephalography (SEEG); noisy neural data