MODELING IN ANTI CANCER DRUG DVMT PRECLINICAL STUDIES THOUGH CLINICAL TRIALS

我们扩大了去年进度报告中所述的工作， 试图从一系列的 来自非EEG相关测量的变量。 我们研究 各种其他基于唤醒的相关性测量。 以下是在一个时代到一个时代的基础上计算和比较 视觉评分唤醒指数：（1）平均值 基于模糊的严重性指数，SI;（2）SI的标准差;（3） （4）潮气量标准差; (5)吸气峰流量的最大呼吸间变化;以及 (6)最大潮气量与最小潮气量之比 音量. 这些变量是从我们的数据库计算的13 睡眠呼吸障碍患者。 基于 预测唤醒敏感度最高的变量（75%）是 SI的标准差。 基于呼吸的变量 预测唤醒的最高特异性是标准差 潮气量（81%）。 然而，与此相关的敏感性 变量仅为68%。 与此同时， 敏感性和特异性的最佳总体评分是 SI的标准偏差。

We extended the work described in last year's progress report by attempting to find improved predictors of arousal from an array of variables derived from non-EEG-related measurements. We examined various other respiratory-based measures for correlation to arousal. The following were calculated on an epoch-to-epoch basis and compared to the visually-scored arousal index: (1) the average value of the fuzzy-based severity index, SI; (2) the standard deviation of SI; (3) average inspired tidal volume; (4) standard deviation of tidal volume; (5) the maximum breath-to-breath change in inspiratory peak flow; and (6) the ratio of the maximum tidal volume over the minimum tidal volume. These variables were computed from our database of 13 patients with sleep-disordered breathing. The respiratory-based variable with the highest sensitivity of predicting arousal (75%) was the standard deviation of SI. The respiratory-based variable with the highest specificity in predicting arousal was the standard deviation of tidal volume (81%). However, the sensitivity associated with this variable was only 68%. Thus, the respiratory-related measure with best overall scores in both sensitivity and specificity was the standard deviation of SI.