Multidimensional Quantification of Macular Cone Activity in Pattern Electroretinography Using Discrete Wavelet Transform

To evaluate discrete wavelet transform (DWT) features as quantitative biomarkers of macular cone function from pattern electroretinography (PERG) in macular-predominant inherited retinal diseases (mpIRDs). In total, 486 PERG recordings from 123 participants were obtained from the PERG-Institute of Applied Ophthalmobiology open-access data set and analyzed. Twenty mother wavelets were screened with an energy-to-entropy ratio criterion; six (haar, sym2, sym4, db4, coif1, fk4) were retained for feature generation. After feature cleaning and correlation pruning, a final set of 141 features was obtained and averaged per participant to avoid visit bias. Group separation was assessed with nonparametric statistics. Inverse-DWT signal reconstruction was performed with the sym2 wavelet to algorithmically determine time-frequency indices needed to preserve N35, P50, and N95 peaks. The smallest set of indices that achieved this was retained. Sym2-D6-2 (38-75 ms, 13-27 Hz) emerged as the top discriminative feature (res = 0.644, common-language effect size = 0.875) and correlated strongly with the clinical macular cone marker |P50-N35| (rcorr = 0.95) across 67 normal participants (262 recordings). Compared with |P50-N35|, the same index showed tighter, nonoverlapping group distributions, a higher diagnostic area under the curve (0.875 vs. 0.835), and a larger effect size (res = 0.644 vs. 0.576). DWT-derived time-frequency features, particularly sym2-D6-2, provide robust, multidimensional biomarkers of macular cone function. These quantitative endpoints hold promise for monitoring disease progression and evaluating therapeutics in mpIRDs. Sym2-D6-2 provides an objective metric of macular cone function that could serve as a quantitative endpoint in mpIRD trials.