ArticleTranslational vision science & technology2022
Time-Frequency Analysis of ERG With Discrete Wavelet Transform and Matching Pursuits for Glaucoma.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.Eye (London, England) · 2025Pooled it
- Wavelet-Based Pattern ERG Biomarkers Outperform Temporal Amplitude Measures for Functional Stratification in Optic Nerve Disease.Translational vision science & technology · 2026Article
- Multidimensional Quantification of Macular Cone Activity in Pattern Electroretinography Using Discrete Wavelet Transform.Translational vision science & technology · 2025Article
- Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- A modified analysis protocol for the PhNR test.Documenta ophthalmologica. Advances in ophthalmology · 2024Article
- Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations.Journal of ophthalmology · 2024Article
- Visual electrophysiology and "the potential of the potentials".Eye (London, England) · 2023Review
- OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms.Bioengineering (Basel, Switzerland) · 2023Article
- Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies.Frontiers in neuroscience · 2023Review
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.