Analysis of EEG Patterns in Vigil and Fatigue States during the Execution of Laparoscopic Tasks

Fatigue decreases
efficiency in performance of several professional activities; therefore, being
in that state could trigger technical mistakes which consequences could be
lethal, such as in health area, where a surgical error due to the absence of
rest can lead to the patient death. For this reason, in this study the vigil
and fatigue (due to sleep) states, that affect cognitive processes in medical
students, were identified through Electroencephalographic (EEG) patterns. The
EEG signals of 18 physician students were analyzed within the theta band (4 - 8
Hz) over fronto-central recording sites, and the alpha band (8 - 13 Hz) rhythms
over temporal and parieto-occipital recording sites during the execution of
laparoscopic tasks before and after their guard. The signal-processing pipeline
consisted in preprocessing based on individual component analysis, absolute
band power estimates, and SVM classification. The f-score to differ between
vigil and fatigue was 90.89%, where the first state showed more slightly identifiable
EEG patterns reaching a sensitivity of 90.18%. The pre-processed EEG signals are shared in this site.

疲劳会降低多项专业活动的执行效率，因此处于疲劳状态可能引发技术失误，其后果甚至可能致命——以医疗领域为例，因缺乏休息导致的手术失误可致使患者死亡。为此，本研究通过脑电图（Electroencephalographic, EEG）模式，识别了影响医学生认知过程的觉醒与睡眠疲劳状态。本研究分析了18名医学生在值守前后执行腹腔镜任务过程中的脑电信号：在额中央记录位点采集的θ波段（4~8 Hz）脑电，以及在颞叶、顶枕叶记录位点采集的α波段（8~13 Hz）脑电节律。信号处理流程包括基于个体成分分析的预处理、绝对波段功率估计，以及支持向量机（SVM）分类。区分觉醒与疲劳状态的F分数达90.89%，其中觉醒状态的脑电模式辨识度更高，灵敏度达90.18%。本网站已共享预处理后的脑电信号。