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. 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. These methods are based respectively on Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). 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). 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 fact, scalp EEG data can often be corrupted by a set of artifacts and are not always served as data of gold standard. A consistent gold standard associated with a high degree of agreement among neuroscientists is required to measure relevant performance of different methods. Therefore, several automatic and semi-automatic methods have been implemented to detect these two neural transients. 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.
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