Objective assessment of visual acuity: a refined model for analyzing the sweep VEP
Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
30694438
DOI
10.1007/s10633-019-09672-z
PII: 10.1007/s10633-019-09672-z
Knihovny.cz E-zdroje
- Klíčová slova
- Sweep VEP, Visual acuity, Visual electrophysiology, Visual evoked potentials,
- MeSH
- dospělí MeSH
- elektroretinografie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- nemoci oční čočky patofyziologie MeSH
- nemoci retiny patofyziologie MeSH
- nemoci zrakového nervu patofyziologie MeSH
- senioři MeSH
- statistické modely MeSH
- zdraví dobrovolníci pro lékařské studie MeSH
- zraková ostrost fyziologie MeSH
- zrakové evokované potenciály fyziologie MeSH
- zrakové testy metody MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
PURPOSE: The aim of this study was to develop a simple and reliable method for the objective assessment of visual acuity by optimizing the stimulus used in commercially available systems and by improving the methods of evaluation using a nonlinear function, the modified Ricker model. METHODS: Subjective visual acuity in the normal subjects was measured with Snellen targets, best-corrected, and in some cases also uncorrected and with plus lenses (+ 1 D, + 2 D, + 3 D). In patients, subjective visual acuity was measured best-corrected using the Freiburg Visual Acuity Test. Sweep VEP recordings to 11 spatial frequencies, with check sizes in logarithmically equidistant steps (0.6, 0.9, 1.4, 2.1, 3.3, 4.9, 7.3, 10.4, 18.2, 24.4, and 36.5 cpd), were obtained from 56 healthy subjects aged between 17 and 69 years (mean 42.5 ± 15.3 SD years) and 20 patients with diseases of the lens (n = 6), retina (n = 8) or optic nerve (n = 6). The results were fit by a multiple linear regression (2nd-order polynomial) or a nonlinear regression (modified Ricker model) and parameters compared (limiting spatial frequency (sflimiting) and the spatial frequency of the vertex (sfvertex) of the parabola for the 2nd-order polynomial fitting, and the maximal spatial frequency (sfmax), and the spatial frequency where the amplitude is 2 dB higher than the level of noise (sfthreshold) for the modified Ricker model. RESULTS: Recording with 11 spatial frequencies allows a more accurate determination of acuities above 1.0 logMAR. Tuning curves fitted to the results show that compared to the normal 2nd-order polynomial analysis, the modified Ricker model is able to describe closely the amplitudes of the sweep VEP in relation to the spatial frequencies of the presented checkerboards. In patients with a visual acuity better than about 0.5 (decimal), the predicted acuities based on the different parameters show a good match of the predicted visual acuities based on the models established in healthy volunteers to the subjective visual acuities. However, for lower visual acuities, both models tend to overestimate the visual acuity (up to ~ 0.4 logMAR), especially in patients suffering from AMD. CONCLUSIONS: Both models, the 2nd-order polynomial and the modified Ricker model performed equally well in the prediction of the visual acuity based on the amplitudes recorded using the sweep VEP. However, the modified Ricker model does not require the exclusion of data points from the fit, as necessary when fitting the 2nd-order polynomial model making it more reliable and robust against outliers, and, in addition, provides a measure for the noise of the recorded results.
University Eye Hospital Hradec Králové Czech Republic
Werner Reichardt Centre for Integrative Neuroscience University of Tuebingen Tuebingen Germany
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Trends Cogn Sci. 2003 Apr;7(4):145-147 PubMed
J Physiol. 1968 Aug;197(3):551-66 PubMed
J Clin Neurophysiol. 2006 Apr;23(2):107-10 PubMed
Biometrics. 1982 Mar;38(1):105-14 PubMed
Ophthalmic Physiol Opt. 2008 Sep;28(5):393-403 PubMed
Doc Ophthalmol. 2013 Feb;126(1):45-56 PubMed
IEEE Trans Biomed Eng. 1965 Apr;12(2):87-94 PubMed
Klin Monbl Augenheilkd. 2002 Sep;219(9):660-7 PubMed
Neuroimage. 2000 Nov;12(5):550-64 PubMed
Doc Ophthalmol. 2008 Sep;117(2):85-91 PubMed
Ann Clin Biochem. 2015 May;52(Pt 3):382-6 PubMed
Vision Res. 1996 Mar;36(6):903-9 PubMed
Lancet. 1986 Feb 8;1(8476):307-10 PubMed
J Physiol. 1970 May;207(3):635-52 PubMed
Exp Brain Res. 1978 Nov 15;33(3-4):535-50 PubMed
Graefes Arch Clin Exp Ophthalmol. 2007 Jul;245(7):965-71 PubMed
Int J Ophthalmol. 2011;4(5):558-66 PubMed
Stat Methods Med Res. 1999 Jun;8(2):135-60 PubMed
Clin Neurophysiol. 2014 Jul;125(7):1471-8 PubMed
Klin Monbl Augenheilkd. 1999 Sep;215(3):175-81 PubMed
Electroencephalogr Clin Neurophysiol. 1970 Jan;28(1):48-54 PubMed
Clin Neurophysiol. 2008 Jun;119(6):1271-80 PubMed
J Physiol. 1966 Dec;187(3):517-52 PubMed
Doc Ophthalmol. 2004 Nov;109(3):239-47 PubMed
BMC Ophthalmol. 2012 Aug 06;12:36 PubMed
Invest Ophthalmol Vis Sci. 1979 Jul;18(7):703-13 PubMed
Invest Ophthalmol Vis Sci. 1993 Jan;34(1):120-9 PubMed
Neurology. 2009 Jan 13;72(2):162-4 PubMed
Doc Ophthalmol. 2017 Dec;135(3):209-218 PubMed
Br J Ophthalmol. 2008 Mar;92(3):396-403 PubMed
Theor Popul Biol. 1998 Dec;54(3):270-93 PubMed
Conf Proc IEEE Eng Med Biol Soc. 2010;2010:4687-90 PubMed
Doc Ophthalmol. 2010 Feb;120(1):111-9 PubMed
Doc Ophthalmol. 2016 Aug;133(1):1-9 PubMed
Optom Vis Sci. 1996 Jan;73(1):49-53 PubMed
Doc Ophthalmol. 2012 Apr;124(2):99-107 PubMed
Ophthalmic Physiol Opt. 1982;2(1):5-23 PubMed
Invest Ophthalmol Vis Sci. 1998 Dec;39(13):2759-68 PubMed
J Physiol. 1975 Nov;252(3):627-56 PubMed
Fortschr Ophthalmol. 1988;85(5):550-4 PubMed
Klin Monbl Augenheilkd. 1992 Feb;200(2):105-9 PubMed
Vision Res. 1974 Dec;14(12):1409-20 PubMed