Discrimination of Visual Evoked Potentials Using Image Processing of Their Time-scale Representations

In this article, the continuous wavelet transform, based on the Mexican Hat function, is used to achieve an automatic processing and classification of visual evoked potentials. Indeed, the representations of the modulus of the wavelet coefficients in the time-scale plan, allowed us to define qualitative criterion for the discrimination of normal and pathological cases. In order to enhance the classification rate, we developed a new method that considers the visual evoked potential's scalogram as an image which is segmented, according to the maximal level of energy density, into many regions of significant interest. The analysis of the resulting segmented image permits to extract a vector of most significant features that will be used to classify normal and pathological signals through SVM classifier. The obtained results show that this technique not only, allows a more reliable distinction between normal and pathological cases, with a very high classification rate (93%) in comparison with the one of the conventional latency measurement (67%), but also, suggests that indications on the progression of the pathology can be provided.