垂直眼电图评估眼疲劳。

Evaluation of eyestrain with vertical electrooculogram.

机构信息

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.

出版信息

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

DOI:10.1016/j.cmpb.2021.106171

PMID:34102561

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.

摘要

背景与目的

随着电脑和智能手机的发展，我们的生活和工作中眼疲劳问题越来越严重。评估眼疲劳有助于预防和缓解眼疲劳。本研究旨在通过分析垂直眼电（VEOG）来评估眼疲劳。

方法

让 21 名年轻受试者在电脑上观看视频，每次观看 120 分钟，在此期间仅使用三个电极采集 VEOG 信号，每 30 分钟回答一次问卷。将 VEOG 信号分为四个 30 分钟的阶段，从 VEOG 信号中提取 VEOG 信号功率概率（VEOGSPP）特征和眨眼特征。眨眼特征包括眨眼次数（BN）、群组眨眼次数（GBN）和眨眼比（GBR）、平均眨眼幅度（Mean_BA）和持续时间（Mean_BD）、50%眨眼平均持续时间（Mean_BD50）、平均闭眼持续时间（Mean_CD）和平均睁眼持续时间（Mean_OD）、50%睁眼早期平均持续时间（Mean_ODE50）和 50%睁眼晚期平均持续时间（Mean_ODL50）、平均眨眼最大上升斜率（Mean_BMRS）和最大下降斜率（Mean_BMFS）的变化。