MONITORING OF HEART RATE VARIABILITY DURING ALCOHOL WITHDRAWAL SYNDROME

该项目的目的是审查措施。 心率变异性(HRV)，持续和无创，在 男性和女性酗酒者因戒酒而被承认。这个 该项目用来检验这样一种假设，即心率变异性的测量可能 用于区分有更多风险的患者的先验亚组 在没有临床表现的情况下严重戒酒 症状学。酒精的外周表现 戒断综合征(AWS)的特点是变化迅速 自主神经的影响。这些时间可能会有所不同，从分钟到小时 至小时，取决于AWS的严重程度和阶段。 因此，HRV措施的发展，是 对信号的非平稳性相对不敏感，可以捕获 短时间测量期间的相关数据是必要的 在协议实施之前。一种算法，使用 相空间分解的概念被发展出来，它表征了 在时间序列测量中显示的噪声类型，例如节拍间 时间间隔(IBI)。与其他员工一致使用不同的 方法，发现健康受试者会产生IBI 具有白噪声和白噪声之间噪声特性的时间序列 棕色噪音。该算法被发现是敏感的， 区分健康的比较对象，以 睁眼或闭眼的范例。相对较短的测量 提取噪声特征所需的时间 时间序列在可接受的误差范围内。相对的 对信号的非平稳性不敏感。我们是 现在从入院接受治疗的人类受试者那里获得数据 酒精戒断综合症。自从我们发现那个酗酒者 受试者有明显不那么复杂的IBI时间序列 (以H衡量)比健康对照受试者，我们会 我想确定戒酒一段时间 4到6周的结果导致复杂性测量回调 至基线(由健康比较对象定义)或如果 降低的信号复杂度在4到6个周期内保持稳定 几周。已经提交了两份与这项工作有关的手稿 以供同行评议。

The purpose of the project is to examine measures of heart rate variability (HRV), continuously and noninvasively, in male and female alcoholics admitted for drinking cessation. The project serves to test the hypothesis that measures of HRV might serve to distinguish a priori sub-groups of patients at risk for more severe alcohol withdrawal in the absence of clinically apparent symptomatology. The peripheral manifestations of alcohol withdrawal syndrome (AWS) are characterized by rapidly changing autonomic influences. These may vary, minute to minute and hour to hour, depending on the severity and stage of AWS. Consequently, the development of HRV measures, which are relatively insensitive to non-stationarity of signal and could capture relevant data during short periods of measurement, was necessary prior to the protocol implementation. An algorithm, using phase-space decomposition, was developed that characterizes the type of noise exhibited in time-series measures such as inter-beat interval (IBI). In agreement with other workers using different methodologies, it was found that healthy subjects generate IBI time-series with noise characteristics between white noise and brown noise. The algorithm was found to be sensitive, distinguishing between healthy comparison subjects measured in an eyes open or eyes closed paradigm. Relatively short measurement times are required to extract the noise characteristics from the time-series within an acceptable degree of error. Relative insensitivity to non-stationarity of signal was demonstrated. We are now obtaining data from human subjects admitted for treatment of alcohol withdrawal syndrome. Since we found that alcoholic subjects had IBI time-series which were significantly less complex (as measured by H) than healthy comparison subjects, we would like to determine whether abstention from alcohol use for a period of 4 to 6 weeks results in a change of the complexity measure back to baseline (as defined by healthy comparison subjects) or if the decreased signal complexity remains stable for a period of 4 to 6 weeks. Two mansucripts related to this work have been submitted for peer review.