Time–Frequency Analysis of ERG With Discrete Wavelet Transform and Matching Pursuits for Glaucoma

PURPOSE: To examine the performance of two time–frequency feature extraction techniques applied to electroretinograms (ERGs) for the prediction of glaucoma severity. METHODS: ERGs targeting the photopic negative response were obtained in 103 eyes of 55 patients with glaucoma. Features from the ERG recordings were extracted using two time–frequency extraction techniques based on the discrete wavelet transform (DWT) and the matching pursuit (MP) decomposition. Amplitude markers of the time-domain signal were also extracted. Linear and multivariate adaptive regression spline (MARS) models were fitted using combinations of these features to predict estimated retinal ganglion cell counts, a measure of glaucoma disease severity derived from standard automated perimetry and optical coherence tomography imaging. RESULTS: Predictive models using features from the time–frequency analyses—using both DWT and MP—combined with amplitude markers outperformed predictive models using the markers alone with linear (P = 0.001) and MARS (P ≤ 0.011) models. For example, the proportions of variance (R(2)) explained by the MARS model using the DWT and MP features with amplitude markers were 0.53 and 0.63, respectively, compared to 0.34 for the model using the markers alone (P = 0.011 and P = 0.001, respectively). CONCLUSIONS: Novel time–frequency features extracted from the photopic ERG substantially added to the prediction of glaucoma severity compared to using the time-domain amplitude markers alone. TRANSLATIONAL RELEVANCE: Substantial information about retinal ganglion cell dysfunction exists in the time–frequency domain of ERGs that could be useful in the management of glaucoma.