Evaluation of eyestrain with vertical electrooculogram

Shuai Li; Dongmei Hao; Bing Liu; Zhijie Yin; Lin Yang; Jie Yu

doi:10.1016/j.cmpb.2021.106171

Evaluation of eyestrain with vertical electrooculogram

Comput Methods Programs Biomed. 2021 Sep:208:106171. doi: 10.1016/j.cmpb.2021.106171. Epub 2021 May 25.

Authors

Shuai Li¹, Dongmei Hao², Bing Liu³, Zhijie Yin¹, Lin Yang¹, Jie Yu⁴

Affiliations

¹ Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
² Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China. Electronic address: haodongmei@bjut.edu.cn.
³ Ophthalmology Department, the University Hospital of Beijing University of Technology, Beijing 100124, China.
⁴ Ophthalmology Department, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.

PMID: 34102561
DOI: 10.1016/j.cmpb.2021.106171

Abstract

Background and objective: Eyestrain 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).

Methods: 21 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).

Results: The 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).

Conclusions: In conclusion, eyestrain induced by watching videos for a long time could be well evaluated by analyzing the VEOG signal.

Keywords: Evaluation; Eyestrain; Feature; Vertical electrooculogram (VEOG).

MeSH terms

Asthenopia*
Blinking
Computers
Electrooculography
Humans
Smartphone