Department of Engineering, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
Department of Sport Science, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
Sci Rep. 2020 Jul 31;10(1):12927. doi: 10.1038/s41598-020-69553-3.
Laparoscopic surgery can be exhausting and frustrating, and the cognitive load experienced by surgeons may have a major impact on patient safety as well as healthcare economics. As cognitive load decreases with increasing proficiency, its robust assessment through physiological data can help to develop more effective training and certification procedures in this area. We measured data from 31 novices during laparoscopic exercises to extract features based on cardiac and ocular variables. These were compared with traditional behavioural and subjective measures in a dual-task setting. We found significant correlations between the features and the traditional measures. The subjective task difficulty, reaction time, and completion time were well predicted by the physiology features. Reaction times to randomly timed auditory stimuli were correlated with the mean of the heart rate ([Formula: see text]) and heart rate variability ([Formula: see text]). Completion times were correlated with the physiologically predicted values with a correlation coefficient of 0.84. We found that the multi-modal set of physiology features was a better predictor than any individual feature and artificial neural networks performed better than linear regression. The physiological correlates studied in this paper, translated into technological products, could help develop standardised and more easily regulated frameworks for training and certification.
腹腔镜手术可能会让人精疲力竭,感到沮丧,而外科医生所经历的认知负荷可能会对患者安全以及医疗保健经济产生重大影响。由于认知负荷随着熟练度的提高而降低,因此通过生理数据对其进行可靠评估有助于在该领域开发更有效的培训和认证程序。我们测量了 31 名新手在腹腔镜练习期间的数据,以提取基于心脏和眼部变量的特征。在双任务设置中,将这些特征与传统的行为和主观措施进行了比较。我们发现特征与传统措施之间存在显著相关性。主观任务难度、反应时间和完成时间可以很好地由生理特征预测。对随机定时听觉刺激的反应时间与心率的平均值([Formula: see text])和心率变异性([Formula: see text])相关。完成时间与生理预测值相关,相关系数为 0.84。我们发现,多模态生理特征集比任何单个特征都更能预测,并且人工神经网络的性能优于线性回归。本文研究的生理相关性转化为技术产品,可以帮助开发标准化且更容易监管的培训和认证框架。