June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Eyelid muscle activity and ERG recording
Author Affiliations & Notes
  • Frank Weng
    St. George's University School of Medicine, Grenada
  • Huy Nguyen
    Ophthalmology, University of Texas Health Science Center - San Antonio, Texas, United States
  • David Drucker
    University of Florida, Florida, United States
  • Radouil T Tzekov
    Ophthalmology, University of South Florida, Florida, United States
  • Footnotes
    Commercial Relationships   Frank Weng, None; Huy Nguyen, None; David Drucker, None; Radouil Tzekov, None
  • Footnotes
    Support  None.
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 772. doi:
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    • Get Citation

      Frank Weng, Huy Nguyen, David Drucker, Radouil T Tzekov; Eyelid muscle activity and ERG recording. Invest. Ophthalmol. Vis. Sci. 2020;61(7):772.

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

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Abstract

Purpose : Excessive eyelid muscle activity could contribute to “noise” associated with electroretinography (ERG) recordings and interfere with the ERG signal itself. This activity could be one of the leading signs of photophobia, a symptom in many ophthalmologic and neurological conditions. Our goal was to quantify the strength of this activity under dark-adapted oscillatory potentials recording conditions of standard ISCEV clinical protocol.

Methods : This is a retrospective review of routine clinical full-field ERG recordings collected at the University of South Florida (Tampa, FL) in patients with variety of clinical diagnoses. Only Dark-adapted oscillatory potential responses (OPs) recorded as part of a standard ISCEV protocol were analyzed. The 30 ms pre-stimulus recording period was used to establish noise baseline. Two periods of muscle activity: at 50-120 ms post-stimulus (R2) and 120-225 ms post stimulus (R3) were measured as root means square (RMS) of the signal in 2 ms bins for each of 9 individual runs. RMS area under the curve (AUC) was calculated and R2/R3 signal estimated based on whether and how much it was exceeding the baseline noise level.

Results : The records of 50 patients (21 M and 29 F; age 53.4 +/- 16.0 yrs.) and were selected for further analysis. Analysis of the baselines noise showed that 31 (6.9%) of right eye recordings and 44 (9.8%) of left eye recordings had noisy baseline; all records with noisy baseline were excluded from further analysis. The percent signal exceeding baseline was averaged across the 9 recordings and evaluated, while the limit of signal exceeding mean baseline noise was set to 5%. Based on that, 35 (70%) of right eyes and 35 (70%) of left eyes had R2 and 36 (72%) of right eyes and 38 (76%) of left eyes had R3 signal. The averaged max signal was 560% (right eyes) and 505% (left eyes) above baseline for R2; however, the maximal peak signal was 4273% (right eyes) and 3502% (left eyes). The averaged max signal was 402% (right eyes) and 394% (left eyes) above baseline for R3 (max peak signal was 1778% (right eyes) and 1810% (left eyes)). Timing analysis showed that the average occurrence of the max signal in R2 was 83.1 ±11.8 ms and 82.3±12.3 ms and R3 it was 159.5±15.7 ms and 160.7±15.7ms for right eye left eyes.

Conclusions : These results demonstrate the feasibility of evaluating the eyelid muscle activity in the ERG signal and provide impetus for further development of this method.

This is a 2020 ARVO Annual Meeting abstract.

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