Evaluation of eyestrain with vertical electrooculogram

Abstract

Background and objectiveEyestrain has been increasingly severe in our lives and works as the progress of computers and smartphones. Evaluating eyestrain helps to prevent and relieve eyestrain. Our study aimed to evaluate eyestrain by analyzing vertical electrooculogram (VEOG). Methods21 young subjects were asked to watch a video on the computer for a totally 120 minutes each, during which the VEOG signal was acquired using only three electrodes, and the questionnaire was answered every 30 minutes. The VEOG signal was divided into four 30-minute phases, from which VEOG signal power probability (VEOGSPP) features and blink features were extracted. The blink features include the changes of blink number (BN), group blinks number (GBN) and ratio (GBR), mean blink amplitude (Mean_BA) and duration (Mean_BD), mean blink duration at 50% (Mean_BD50), mean closing duration (Mean_CD) and opening duration (Mean_OD), mean opening duration at early 50% (Mean_ODE50) and late 50% (Mean_ODL50), mean blink maximum rising slope (Mean_BMRS) and falling slope (Mean_BMFS). ResultsThe results showed that the VEOGSPP in the high-frequency band (0.8-6.3Hz), BN, GBN, and GBR significantly increased while the VEOGSPP in the low-frequency band (0.1-0.4Hz), Mean_BA, Mean_OD, and Mean_ODL50 significantly decreased with eyestrain (P<0.05). ConclusionsIn conclusion, eyestrain induced by watching videos for a long time could be well evaluated by analyzing the VEOG signal.