Time-Frequency Analysis of Photopic Negative Response in CRVO Patients

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 (R2) 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.

Oscillatory Potentials in Achromatopsia as a Tool for Understanding Cone Retinal Functions

Achromatopsia (ACHM) is an inherited autosomal recessive disease lacking cone photoreceptors functions. In this study, we characterize the time-frequency representation of the full-field electroretinogram (ffERG) component oscillatory potentials (OPs), to investigate the connections between photoreceptors and the inner retinal network using ACHM as a model. Time-frequency characterization of OPs was extracted from 52 controls and 41 achromat individuals. The stimulation via ffERG was delivered under dark-adaptation (DA, 3.0 and 10.0 cd·s·m-2) to assess mixed rod-cone responses. The ffERG signal was subsequently analyzed using a continuous complex Morlet transform. Time-frequency maps of both DA conditions show the characterization of OPs, disclosing in both groups two distinct time-frequency windows (~70-100 Hz and >100 Hz) within 50 ms. Our main result indicates a significant cluster (p < 0.05) in both conditions of reduced relative power (dB) in ACHM people compared to controls, mainly at the time-frequency window >100 Hz. These results suggest that the strongly reduced but not absent activity of OPs above 100 Hz is mostly driven by cones and only in small part by rods. Thus, the lack of cone modulation of OPs gives important insights into interactions between photoreceptors and the inner retinal network and can be used as a biomarker for monitoring cone connection to the inner retina.

Application of a Mapping Method in the Analysis of Electroretinogram in Patients with Retinitis Pigmentosa

PURPOSE

Retinitis pigmentosa (RP) is a group of degenerative retinal diseases characterized by mutations in genes affecting retinal pigment epithelium (RPE) function, as well as mutations directly involving photoreceptors. This paper aims to evaluate a nonlinear method to distinguish between the RP and normal eye based on the Electroretinogram (ERG) signal.

METHOD

ERG signal was recorded from 28 eyes of patients with the RP and 32 normal eyes. The ERG signal consists of four different stimuli, including two dark-adapted and two light-adapted stimuli. The time-domain analysis includes the amplitude and implicit time to consider the robustness of the nonlinear method. A parabolic mapping method was performed, and two criteria (Theta angle and density) extracted from the parabola were compared for both groups.

RESULTS

The results showed that a-wave's amplitude and implicit time significantly changed in the dark- and light-adapted stimuli. The amplitude of the b-wave showed significant changes in all stimuli. However, the implicit time of b-wave had a significant increase only in the dark-adapted 3.0 ERG. Both nonlinear criteria showed significant changes in the RP group for all the stimuli. The p-values of dark-adapted 3.0 (p = .0121), dark-adapted 10.0 (p = .0014), light-adapted 3.0 (p = .0119), and flicker 30 Hz (p = .0323) showed significant differences. Using the density criterion, the statistical test demonstrated a significant difference between the RP and healthy normal group in dark-adapted 3.0 (p = .0076), dark-adapted 10.0 (p = .0024), light-adapted 3.0 (p = .0021), and flicker 30 Hz (p = .0165).

CONCLUSION

The proposed features have made it possible to distinguish between healthy and RP eyes. This method might be helpful in early diagnosis.

Continuous wavelet transform analysis of ERG in patients with diabetic retinopathy

PURPOSE

Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide. Non-proliferative diabetic retinopathy (NPDR) is a stage of the disease that contains morphological and functional disruption of the retinal vasculature and dysfunction of retinal neurons. This study aimed to compare time and time-frequency-domain analysis in the evaluation of electroretinograms (ERGs) in subjects with NPDR.

METHOD

The ERG responses were recorded in 16 eyes from 12 patients with NPDR and 24 eyes from 12 healthy subjects as the control group. The implicit time, amplitude, and time-frequency-domain parameters of photopic and scotopic ERGs were analyzed.

RESULTS

The implicit times of b-waves in the dark-adapted 10.0 (P = 0.0513) and light-adapted 3.0 (P = 0.0414) were significantly increased in the NPDR group. The amplitudes of a- and b-wave showed a significantly decreased dark-adapted 10.0 (P = 0.0212; P = 0.0133) and light-adapted 3.0 (P = 0.0517; P = 0.0021) ERG of the NPDR group. The Cohen's d effect size had higher values in the amplitude of dark-adapted 10.0 b-wave (|d|= 1.8058) and amplitude of light-adapted 3.0 b-wave (|d|= 1.9662). The CWT results showed that the frequency ranges of the dominant components in dark-adapted 10.0 and light-adapted 3.0 ERG were decreased in the NPDR group compared to the healthy group (P < 0.05). The times associated with the NDPR group's dominant components were increased compared to normal eyes in both dark-adapted 10.0 and light-adapted 3.0 ERG (P < 0.05). All Cohen's d effect sizes of the implicit times and dominant frequency components were on a large scale (|d|> 1).

CONCLUSION

These findings suggest that the time and time-frequency parameters of both photopic and scotopic ERGs can be good indicators for DR. However, time-frequency-domain analysis could present more information might be helpful in the assessment of the DR severity.