PDF(Pubmed)
Abstract：
Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is visionary in the understanding of visual signal processing and will facilitate the deepening of our knowledge concerning anesthetic research. Methods: X-gradient boosting models were used to classify the cortical response to visual stimulation (no light vs. light stimulations and two lights with different brightnesses). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), and training across all but one participant with prediction performed in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulations (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96 ± 0.05, accuracyindividual: 0.94 ± 0.05; AUCROConeout: 0.98 ± 0.04; accuracyoneout: 0.96 ± 0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between light stimulations with different brightnesses (AUCROCall: 0.97; accuracyall: 0.91; AUCROCindividual: 0.98 ± 0.04, accuracyindividual: 0.96 ± 0.04; AUCROConeout: 0.96 ± 0.05; accuracyoneout: 0.93 ± 0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or light stimulations using visual evoked potentials in awake female and male volunteers with eyes closed. Our findings may open new possibilities for the use of visual evoked potentials in the clinical and intraoperative setting.
摘要：
背景/目标：我们定义了机器学习算法的价值，以区分无光或任何光刺激的EEG反应。以及在眼睑闭合的清醒志愿者中具有不同亮度的光刺激之间。这种利用EEG分析的新方法在理解视觉信号处理方面具有远见卓识，将有助于加深我们对麻醉研究的认识。方法：使用X梯度增强模型对皮层对视觉刺激的反应进行分类（无光与光刺激和两个不同亮度的灯)。对于这两种分类中的每一种，测试了三种情况：所有参与者的训练和预测(全部)，一个参与者(个人)的训练和预测，并在除一名参与者外的所有参与者中进行训练，并在参与者被遗漏的情况下进行预测(一人出局)。结果：包括94名白种人。机器学习算法在区分无光和任何光刺激方面具有非常高的预测价值和准确性（AUCROCall：0.96;准确性：0.94;AUCROCindividual：0.96±0.05，准确性个体：0.94±0.05;AUCROConeout：0.98±0.04;准确性：0.96±0.04）。机器学习算法在区分不同亮度的光刺激方面具有很高的预测性和准确性（AUCROCall：0.97；准确性：0.91；AUCROCindividual：0.98±0.04，准确性：0.96±0.04；AUCROConeout：0.96±0.05；准确性：0.93±0.06）。两种分类任务的预测价值和准确性在男性和女性之间具有可比性。结论：机器学习算法可以在闭眼的清醒女性和男性志愿者中使用视觉诱发电位几乎连续且可靠地区分对无光或光刺激的皮层EEG反应。我们的发现可能为在临床和术中使用视觉诱发电位开辟了新的可能性。
