Time-Frequency Analysis of Photopic Negative Response in CRVO Patients

Behbahani, Soroor; Ramezani, Alireza; Moridani, Mohammad Karimi; Sabbaghi, Hamideh

doi:10.1080/08820538.2020.1781905

Cited by 8 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forte and colleagues 33 showed that the Morlet wavelet could be used to isolate oscillatory potentials in rat ERGs. Behbahani and colleagues 34 used a Mexican hat wavelet to determine the dominant frequencies associated with the PhNR in patients with central retinal vein occlusion and found that the dominant frequency decreased. However, the CWT has not thus far found clinical utility for glaucoma.…”

Section: Introductionmentioning

confidence: 99%

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

Sarossy

Crowston

Kumar

et al. 2022

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Sarossy

Crowston

Kumar

et al. 2022

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

show abstract

“…Another method, known as continuous wavelet transform, consists in extracting simultaneously both time and frequency information through the convolution of a kernel (wavelet) on the target trace. The characterization of the ffERG signal in the time and frequency domains has emerged as a suited method for assessing retinal components in normal and diseased retinae [ 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

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

Righetti

Kempf

Braun

et al. 2021

IJMS

View full text Add to dashboard Cite

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.

show abstract

“…Processing methods attempt to extract the maximum information about the proper functioning or non-functioning of the related organs. Different signal processing methods have been applied to ERG, including timedomain, frequency-domain, and time-frequency domain analyses [20][21][22][23][24][25][26]. Besides, nonlinear features have been used to analyze the ERG [27][28].…”

Section: Introductionmentioning

confidence: 99%

Electroretinogram Analysis In Patients With Non-Proliferative Diabetic Retinopathy

Maleki

Behbahani

Ahmadieh

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Purpose: Non-proliferative diabetic retinopathy (NPDR) is the earliest stage of diabetic eye disease. Microscopic changes occur in the blood vessels of the eye in NPDR. The changes typically do not produce symptoms and are not visible to the naked eye. This paper aims to investigate a method for distinguishing NPDR based on Electroretinogram (ERG).Method: The ERG responses were recorded in 20 eyes from 14 patients with NPDR and 20 eyes from 20 healthy subjects as the control group. The responses of three standard stimuli were collected for both groups. Time-domain parameters, including amplitudes and implicit time, and a nonlinear criterion, were used to differentiate the groups. Results: This study showed that implicit time and amplitude of b-wave in dark-adapted 10.0 ERG and amplitude and implicit time of light-adapted flicker 30 Hz could distinguish between controls and NPDR groups. Theta values obtained for dark-adapted 10.0 ERG (p=0.0019), light-adapted 3.0 ERG (p=0.0021), and light-adapted flicker 30 Hz (p=0.0023) had significant differences between the groups. Conclusion: The proposed features have made it possible to distinguish between healthy and NPDR eyes. Choosing an appropriate method can effectively evaluate inner retinal dysfunction, especially in diabetic retinopathy.

show abstract

Time-Frequency Analysis of Photopic Negative Response in CRVO Patients

Cited by 8 publications

References 43 publications

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

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

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

Electroretinogram Analysis In Patients With Non-Proliferative Diabetic Retinopathy

Contact Info

Product

Resources

About