June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Comparison of automated evaluation of the fast and slow oscillations Electro-oculogram and normative data
Author Affiliations & Notes
  • Torsten Strasser
    Institute for Ophthalmic Research, University of Tuebingen, Tuebingen, Germany
  • Katarina Stingl
    Institute for Ophthalmic Research, University of Tuebingen, Tuebingen, Germany
  • Hana Langrová
    Institute for Ophthalmic Research, University of Tuebingen, Tuebingen, Germany
    Department of Ophthalmology, Charles University in Prague, Prague, Czech Republic
  • Nurgül Düzenli
    Institute for Ophthalmic Research, University of Tuebingen, Tuebingen, Germany
  • Anne Kurtenbach
    Institute for Ophthalmic Research, University of Tuebingen, Tuebingen, Germany
  • Eberhart Zrenner
    Institute for Ophthalmic Research, University of Tuebingen, Tuebingen, Germany
    Werner Reichardt Center for Integrative Neuroscience, University of Tuebingen, Tuebingen, Germany
  • Footnotes
    Commercial Relationships Torsten Strasser, None; Katarina Stingl, None; Hana Langrová, None; Nurgül Düzenli, None; Anne Kurtenbach, None; Eberhart Zrenner, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 462. doi:
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      Torsten Strasser, Katarina Stingl, Hana Langrová, Nurgül Düzenli, Anne Kurtenbach, Eberhart Zrenner; Comparison of automated evaluation of the fast and slow oscillations Electro-oculogram and normative data. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):462.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

The electro-oculogram (EOG) is a diagnostic tool measuring components with different time courses (fast and slow oscillations, FO and SO) of light induced changes in the electrical resting potential across the retinal pigment epithelium (RPE). Here we present two algorithms for automated evaluation of SO and FO EOG, a comparison with manual evaluation, and normative data.

 
Methods
 

SO and FO EOG were recorded in healthy volunteers (SO: male/female n = 24/28, FO: n = 13/22; age: 13 - 69 yrs) according to ISCEV standards using a Diagnosys Espion e².<br /> SO amplitudes were determined by a square-wave fit (FIT), built-in analysis (RMS), and manual cursor placement (MAN). Subsequently, dark trough and light peak were determined by a cubic polynomial fit, and the Arden ratio was calculated.<br /> FO were evaluated using automated marker placement (AUT), and by fitting a sine wave to the envelope generated with an ADSR filter (ENV). Subsequently, the peak-/trough ratio was calculated.<br /> Norms were stratified for age and gender. The study was approved by the local ethics committee and performed in accordance to the Declaration of Helsinki.

 
Results
 

Manual and automated evaluation did not show significant differences regarding the Arden ratio (MAN / FIT: p = .227; MAN / RMS: p = .096) (Fig. 2b). Normative values for the Arden ratio (median (5th-95th percentile)): FIT: 234 % (167 % - 381 %); MAN: 228 % (167 % - 366 %); RMS: 217 % (164 % - 365 %). Fig. 2a lists additional values for dark trough and light peak. The Arden ratio does not correlate with the gender or age, except in manual evaluation (Fig. 2c).<br /> Peak/trough ratios determined with AUT and ENV are significantly different (p = < .0001). Normative ranges for FO peak/trough ratio (mean ± sd): ENV: 1.21 ± 0.08; AUT: 1.76 ± 0.65. There is no correlation between ratio and age (p = .281) or gender (p = .868).

 
Conclusions
 

Automated analysis of slow and fast oscillations EOG avoids time-consuming manual evaluation. Manual and automated approaches of SO show no significant difference. Automated evaluation of FO is significantly different to built-in evaluation.  

 
Fig. 1: a) SO manual marker placement. b) SO RMS analysis. c) SO square-wave fit. d) SO light peak / dark trough evaluation. e) FO envelope curve.
 
Fig. 1: a) SO manual marker placement. b) SO RMS analysis. c) SO square-wave fit. d) SO light peak / dark trough evaluation. e) FO envelope curve.
 
 
Fig. 2: a) Normative data for SO. b) Comparison of manual and automated evaluation of the Arden ratio. c) Effect of age and gender on Arden ratio
 
Fig. 2: a) Normative data for SO. b) Comparison of manual and automated evaluation of the Arden ratio. c) Effect of age and gender on Arden ratio

 
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