Wavelet decomposition analysis in the two-flash multifocal ERG in early glaucoma: a comparison to ganglion cell analysis and visual field

Abstract

Purpose: To further improve analysis of the two-flash multifocal electroretinogram (2F-mfERG) in glaucoma in regard to structure-function analysis, using discrete wavelet transform (DWT) analysis.

Methods: Sixty subjects [35 controls and 25 primary open-angle glaucoma (POAG)] underwent 2F-mfERG. Responses were analyzed with the DWT. The DWT level that could best separate POAG from controls was compared to the root-mean-square (RMS) calculations previously used in the analysis of the 2F-mfERG. In a subgroup analysis, structure-function correlation was assessed between DWT, optical coherence tomography and automated perimetry (mf103 customized pattern) for the central 15°.

Results: Frequency level 4 of the wavelet variance analysis (144 Hz, WVA-144) was most sensitive (p < 0.003). It correlated positively with RMS but had a better AUC. Positive relations were found between visual field, WVA-144 and GCIPL thickness. The highest predictive factor for glaucoma diagnostic was seen in the GCIPL, but this improved further by adding the mean sensitivity and WVA-144.

Conclusions: mfERG using WVA analysis improves glaucoma diagnosis, especially when combined with GCIPL and MS.

Keywords: Discrete wavelet analysis; Ganglion cell–inner plexiform layer; Glaucoma; Multifocal electroretinogram; Optical coherence tomography.

Fig. 1

DWT analysis applied to the mfERG response from a control (left) and a patient (right). Top graphical representation of the 2F-mfERG M-sequence used here (MOFOFO), with frames displaced in time in order to better correspond visually to the recorded response. The original signal from one hexagon of the mfERG (waveform inside box on top) can be decomposed into many frequency levels, depending on the length of the time series. The first level (1211 Hz) corresponds to high frequencies (noise), while the highest level (11 Hz) corresponds to the lowest frequencies. For each frequency level, the vertical lines represent individual wavelet coefficients. For each level, the variance between these coefficients is computed and subjected to further analysis as the WVA (wavelet variance). Legend: DC direct component; IC1 first induced component; IC2 second induced component

Fig. 2

This figure summarizes the results of our decomposition. For each group, the box plots show the distribution of the wavelet variance (WVA, see Fig. 1) considering each location (19 focal mfERG waveforms) for each subject within each frequency level analyzed. Variance at frequency level 4, that is at 144 Hz, was the most sensitive distinguishing parameter (p = 0.015, red box). Legend: PPG pre-perimetric glaucoma; NTG normal-tension glaucoma; HTG high-tension glaucoma

Fig. 3

Graphical representation of corresponding areas from each examination (GCIPL, mf103-pattern and 2F-mfERG) compared in the study. Legends: D diameter; GCIPL ganglion cell–inner plexiform layer; m103-pattern Octopus pattern with 103 stimulus points; 2F-mfERG double-flash multifocal electroretinogram

Fig. 4

Graphical representation of structure–function relationship between each examination (GCIPL, MS and WVA-144) and its respective p values. Legend: GCIPL ganglion cell–inner plexiform layer in µm; WVA-144 wavelet variance analysis 144 Hz (in logarithm); MS mf103-field mean sensitivity from mf103-pattern exclude “protocol” in linear values

Fig. 5

For the central 15 degrees, this figure shows the relationship between GCIPL thickness map sectors and the 2F-mfERG responses to the corresponding hexagons. Green: GCIPL = ganglion cell–inner plexiform layer thickness map (retina view). *p values <0.05 were considered significant. Calculations were adjusted for age and gender