基于脑电图信号的疲劳对腹腔镜手术模拟的影响。

IF 1.3 Q3 SURGERY
Minimally Invasive Surgery Pub Date : 2018-05-02 eCollection Date: 2018-01-01 DOI:10.1155/2018/2389158
Nyakuru Z Ndaro, Shu-Yi Wang
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引用次数: 13

摘要

背景:随着最近技术的进步,人们对基于电生理信号作为监测大脑活动的手段来研究疲劳越来越感兴趣。虽然一些现有的作品将疲劳与表现联系起来,但其他作品则将两者视为独立的实体。因此,为了患者的安全,我们必须探索这个复杂的问题,特别是在腹腔镜训练中。目的:利用脑电图(EEG)信号探讨疲劳对腹腔镜手术训练效率和准确性的影响。材料与方法:20名大学生在腹腔镜模拟器上完成peg转移任务,实时记录每名被试的脑电图信号。为了监测疲劳程度,设计了一种基于疲劳分析算法的实时疲劳监测系统,利用脑电图的α (α)和θ (θ)节律进行监测。设计了基于MATLAB平台的数据采集和疲劳分析模块。BrainLink用于记录脑电图信号,并通过蓝牙将其无线发送到个人电脑。利用盲源分离(BSS)去除脑电信号中的伪影,并利用小波分析提取α和θ节律。基于回归模型和马氏距离(DC)对疲劳进行评价,并利用受试者工作特征(ROC)曲线分析从实验结果中确定疲劳阈值。结果:完成时间和错误次数在前几次试验中表现为递减函数,随后随着疲劳程度的增加而增加。结果表明,受试者的学习曲线在第13次试验前一直呈上升趋势,在第13次试验后由于疲劳,学习曲线逐渐下降。结论:回归分析表明,在一系列试验中,重复训练中的peg转移任务有显著的学习和疲劳效应。然而,为了使培训有效和高效,应该在培训期间进行监控,以观察学员在学习曲线中获得最大学习收益的位置。此外,在完井时间和误差方面,疲劳是效率和精度的重要指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Effects of Fatigue Based on Electroencephalography Signal during Laparoscopic Surgical Simulation.

Effects of Fatigue Based on Electroencephalography Signal during Laparoscopic Surgical Simulation.

Effects of Fatigue Based on Electroencephalography Signal during Laparoscopic Surgical Simulation.

Effects of Fatigue Based on Electroencephalography Signal during Laparoscopic Surgical Simulation.

Background: Following recent advances in technology, there is a growing interest in studying fatigue based on electrophysiological signals as a means of monitoring brain activity. While some existing works relate fatigue to performance, others consider the two as independent entities. Therefore, we must explore this intricate issue, particularly in laparoscopic training, for the sake of patient safety.

Objective: This paper explores and evaluates effects of fatigue on efficiency and accuracy based on laparoscopic surgical training using Electroencephalography (EEG) signal.

Materials and methods: 20 college students performed peg transfer task on laparoscopic simulator, with real-time recording of EEG signals for each subject. To monitor degree of fatigue, a real-time fatigue monitoring system based on fatigue analysis algorithm was designed through the use of EEG in alpha (α) and theta (θ) rhythms. We designed data acquisition and fatigue analysis modules based on MATLAB platform. BrainLink was used to record EEG signals and send them to personal computer wirelessly via Bluetooth. While artifacts from the captured EEG signals were removed using Blind Source Separation (BSS), α and θ rhythms were extracted using wavelet analysis. Fatigue was evaluated based on Regression Model and Mahalanobis Distance (DC ), and its threshold was determined from the experimental results using Receiver Operating Characteristic (ROC) curve analysis.

Results: Completion time and number of errors behaved like a decreasing function during the first few trials while increasing afterwards with the increasing of perceived fatigue level. The results indicate that learning curve of the subjects is increasing until 13th trials when they have attained maximum learning benefits and decreases afterwards due to fatigue.

Conclusion: Regression analysis shows that there are significant learning and fatigue effects when peg transfer task in the training is repeated in a series of trials. However, for the training to be effective and efficient, there should be monitoring during the training to observe where in the learning curve a trainee gains maximum learning benefits. Furthermore, fatigue is a significant indicator of efficiency and accuracy in terms of completion time and errors, respectively.

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CiteScore
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审稿时长
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