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The paper discusses creating a wavelet-based feature space for the classification of transient pattern electroretinograms (PERGs)—signals utilized in ophthalmology to evaluate the state of the retina. Discrete wavelet transform (DWT) can provide compact signal description, which is more accurate than time-domain data. A procedure for the proper choice of transform parameters is proposed. Both time-domain and wavelet features of these waveforms are visualized using principal components analysis. Separability of feature spaces is compared using k-means clustering algorithm. The results suggest that PERG waveforms are better separable when represented by DWT coefficients of full time-domain signal, than in traditional peak-based feature space.