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），连续和非侵入性， 男性和女性酗酒者因戒酒而入院。的 该项目旨在检验HRV测量可能 用于区分处于更高风险的患者的先验亚组， 严重的酒精戒断，在没有临床上明显的 医学。酒精的外周表现 戒断综合征（AWS）的特点是快速变化， 自主神经的影响这些可能会有所不同，分钟到分钟和小时 根据AWS的严重程度和阶段， 因此，HRV测量的发展， 对信号的非平稳性相对不敏感， 在短时间内测量相关数据，是必要的 在协议实施之前。一种算法，使用 相空间分解，开发的特点， 在时间序列测量中表现出的噪声类型，例如 间隔（IBI）。与其他使用不同 方法，发现健康受试者产生IBI， 噪声特性介于白色噪声和 棕色噪音该算法被发现是敏感的， 区分健康对照受试者， 眼睛睁开或眼睛闭上的范例。相对较短的测量 需要时间来从噪声中提取噪声特性。 时间序列在可接受的误差范围内。相对 证明了对信号非平稳性不敏感。我们 现在从接受治疗的人类受试者中获得数据， 酒精戒断综合征自从我们发现那个酒鬼 受试者的IBI时间序列明显不那么复杂 (as测量的H）比健康的比较对象，我们会 我想确定一段时间内戒酒 4到6周的时间会导致复杂性指标的变化， 至基线（由健康对照受试者定义），或者如果 降低的信号复杂度在4到6个周期内保持稳定 周已经提交了两份与这项工作有关的手稿 进行同行评审

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.