Analysis of pattern electroretinogram signals of early primary open-angle glaucoma in discrete wavelet transform coefficients domain

Hassankarimi, Homa; Noori, Seyed Mohammad Reza; Jafarzadehpour, Ebrahim; Yazdani, Shahin; Radinmehr, Fatemeh

doi:10.1007/s10792-019-01077-w

Cited by 7 publications

(6 citation statements)

References 46 publications

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“…Discrete wavelet transform analysis has been applied to other clinical conditions. For instance, the multifocal ERG and the pattern ERG reveal a positive association with primary open angle glaucoma ( Brandao et al, 2017 ; Hassankarimi et al, 2019 ). In congenital stationary night blindness, DWT analysis of the ERG and multifocal ERG highlighted the known absence of the ON-pathway involvement in the waveforms ( Dorfman et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

et al. 2022

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BackgroundTo evaluate the electroretinogram waveform in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) using a discrete wavelet transform (DWT) approach.MethodsA total of 55 ASD, 15 ADHD and 156 control individuals took part in this study. Full field light-adapted electroretinograms (ERGs) were recorded using a Troland protocol, accounting for pupil size, with five flash strengths ranging from –0.12 to 1.20 log photopic cd.s.m–2. A DWT analysis was performed using the Haar wavelet on the waveforms to examine the energy within the time windows of the a- and b-waves and the oscillatory potentials (OPs) which yielded six DWT coefficients related to these parameters. The central frequency bands were from 20–160 Hz relating to the a-wave, b-wave and OPs represented by the coefficients: a20, a40, b20, b40, op80, and op160, respectively. In addition, the b-wave amplitude and percentage energy contribution of the OPs (%OPs) in the total ERG broadband energy was evaluated.ResultsThere were significant group differences (p < 0.001) in the coefficients corresponding to energies in the b-wave (b20, b40) and OPs (op80 and op160) as well as the b-wave amplitude. Notable differences between the ADHD and control groups were found in the b20 and b40 coefficients. In contrast, the greatest differences between the ASD and control group were found in the op80 and op160 coefficients. The b-wave amplitude showed both ASD and ADHD significant group differences from the control participants, for flash strengths greater than 0.4 log photopic cd.s.m–2 (p < 0.001).ConclusionThis methodological approach may provide insights about neuronal activity in studies investigating group differences where retinal signaling may be altered through neurodevelopment or neurodegenerative conditions. However, further work will be required to determine if retinal signal analysis can offer a classification model for neurodevelopmental conditions in which there is a co-occurrence such as ASD and ADHD.

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Section: Discussionmentioning

confidence: 99%

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

et al. 2022

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“…Behbahani et al [99] determined the effect of CRVO on PhNR and RGCs performances based on CWT. The PhNR of ffERG of patients was compared to the fellow normal eyes.…”

Section: Comparison Of Time-frequency Analysis-based Studiesmentioning

confidence: 99%

Feature Extraction Methods for Electroretinogram Signal Analysis: A Review

2021

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Feature extraction is an essential aspect of Electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance.

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“…In the paper [ 46 ], the analysis of pattern electroretinogram (PERG) signals in the domain of discrete wavelet transform (DWT) coefficients was investigated. The authors chose the DWT as the transform method due to its capability to capture both time and frequency information simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…This application of signal analysis may provide additional and complementary information about the underlying neural generators of the ERG and supports the advancement of the use of the ERG as a clinical test of retinal and central nervous system function [ 52 , 53 ]. The application of signal analysis has been widely used to evaluate biological signals in cardiac [ 54 ] and EEG recordings [ 55 , 56 ]; however, this approach is relatively uncommon in ophthalmology, with DWT used to evaluate glaucoma [ 45 , 46 , 48 ] and neurodevelopmental disorders [ 42 , 48 ]. Here, we compare CWT with DWT in the analysis of recovery from an experimental model of endophthalmitis that has not been used previously.…”

Section: Introductionmentioning

confidence: 99%

OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms

et al. 2023

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Background: The electroretinogram is a clinical test used to assess the function of the photoreceptors and retinal circuits of various cells in the eye, with the recorded waveform being the result of the summated response of neural generators across the retina. Methods: The present investigation involved an analysis of the electroretinogram waveform in both the time and time–frequency domains through the utilization of the discrete wavelet transform and continuous wavelet transform techniques. The primary aim of this study was to monitor and evaluate the effects of treatment in a New Zealand rabbit model of endophthalmitis via electroretinogram waveform analysis and to compare these with normal human electroretinograms. Results: The wavelet scalograms were analyzed using various mother wavelets, including the Daubechies, Ricker, Wavelet Biorthogonal 3.1 (bior3.1), Morlet, Haar, and Gaussian wavelets. Distinctive variances were identified in the wavelet scalograms between rabbit and human electroretinograms. The wavelet scalograms in the rabbit model of endophthalmitis showed recovery with treatment in parallel with the time-domain features. Conclusions: The study compared adult, child, and rabbit electroretinogram responses using DWT and CWT, finding that adult signals had higher power than child signals, and that rabbit signals showed differences in the a-wave and b-wave depending on the type of response tested, while the Haar wavelet was found to be superior in visualizing frequency components in electrophysiological signals for following the treatment of endophthalmitis and may give additional outcome measures for the management of retinal disease.

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Analysis of pattern electroretinogram signals of early primary open-angle glaucoma in discrete wavelet transform coefficients domain

Cited by 7 publications

References 46 publications

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

Feature Extraction Methods for Electroretinogram Signal Analysis: A Review

OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms

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