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A Bayesian Algorithm for Anxiety Index Prediction Based on Cerebral Blood Oxygenation in the Prefrontal Cortex Measured by Near Infrared Spectroscopy

机译：基于近额光谱法测量的基于脑血氧的焦虑指数预测呼吸指数预测差异算法

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Stress-induced psychological and somatic diseases are virtually endemic nowadays. Written self-report anxiety measures are available; however, these indices tend to be time consuming to acquire. For medical patients, completing written reports can be burdensome if they are weak, in pain, or in acute anxiety states. Consequently, simple and fast non-invasive methods for assessing stress response from neurophysiological data are essential. In this paper, we report on a study that makes predictions of the state-trait anxiety inventory (STAI) index from oxyhemoglobin and deoxyhemoglobin concentration changes of the prefrontal cortex using a two-channel portable near-infrared spectroscopy device. Predictions are achieved by constructing machine learning algorithms within a Bayesian framework with nonlinear basis function together with Markov Chain Monte Carlo implementation. In this paper, prediction experiments were performed against four different data sets, i.e., two comprising young subjects, and the remaining two comprising elderly subjects. The number of subjects in each data set varied between 17 and 20 and each subject participated only once. They were not asked to perform any task; instead, they were at rest. The root mean square errors for the four groups were 6.20, 6.62, 4.50, and 6.38, respectively. There appeared to be no significant distinctions of prediction accuracies between age groups and since the STAI are defined between 20 and 80, the predictions appeared reasonably accurate. The results indicate potential applications to practical situations such as stress management and medical practice.
机译：应力引起的心理和躯体疾病都是时下流行的几乎。写自我报告的焦虑措施可供选择;但是，这些指标往往是耗时的收购。对于医疗的患者，在完成书面报告成为了累赘，如果自己是弱者，疼痛，或在急性焦虑状态。因此，用于评估从神经生理学数据应激反应简单且快速的非侵入性方法是必不可少的。在本文中，我们在研究，使状态特质焦虑量表的预测（STAI）指数从氧合血红蛋白和脱氧血红蛋白使用双通道便携式近红外光谱装置的前额叶皮层的浓度的变化情况。预测是通过用马尔可夫链蒙特卡洛实施具有非线性基函数贝叶斯框架内构建的机器学习算法一起实现。在本文中，预测实验针对四个不同的数据集进行的，即两个包括年轻受试者，并且剩余的两个包括老年受试者。的每个数据对象的数目组17和20之间变化，并且每个受试者参加一次。他们没有被要求执行任何任务;相反，他们在休息。均方根误差为四个组分别为6.20，6.62，4.50，6.38和分别。似乎有年龄组的STAI之间，因为预测精度的不显著区别20-80之间定义，预测出现了相当准确的。结果表明潜在应用到实际情况，如压力管理和医疗实践。

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