Research Resource for Complex Physiologic Signals

NIH/NCRR复杂生理信号研究资源于9月份成立。1999年，为了 加快研究进展，推动生物医学信号新的基础和临床研究。这是竞争激烈的 更新申请建议加强和扩大这一多学科资源的组成部分，包括： 3hysioBank--一个庞大的、不断增长的生理信号和相关数字记录的档案 数据目前包括大约40个数据库，其中包括来自健康人群的心肺、神经和其他生物医学信号 受试者和具有重大公共卫生影响的各种情况的患者，包括突发心脏病 死亡、充血性心力衰竭、帕金森氏症、睡眠呼吸暂停和衰老。核心和协作研究 支持这一构成部分开发新的数据库和专门软件，以协助这一进程。 HysioToolkit--用于生理信号处理和分析的大型且不断增长的开源软件库， 使用经典技术和基于统计的新方法来检测重要的生理事件 人体动力学和非线性动力学，信号的交互显示和表征，新数据库的创建， 生理和其他信号的模拟，分析方法的评价和比较，以及复合体的分析。 女神。为PhysioToolkit贡献软件的研究项目的一个统一主题是提取隐藏的 来自生物医学信号的信息，可能在医学上具有诊断或预后价值的信息，或 在基础研究中的解释或预测能力。 Physionet--该资源的网站(www.Physionet.org)提供对PhysioBank和PhysioToolkit的免费访问，以及 用于数据和算法的讨论和合作分析的设施。Physionet提供了越来越多的教程，以 协助培训研究人员、临床医生和学生进行复杂信号分析。Physionet还举办了一场高度 一系列成功的开放挑战，集中研究努力，推动在重要的基础和 临床研究问题。 我们还提议开发新的数据分析和档案生物技术工具，以支持 NCRR的普通临床研究中心，并扩大我们针对广泛的核心和合作研究 一系列基本和临床问题，包括婴儿呼吸暂停和睡眠呼吸障碍，步态障碍和摔倒 老年人和帕金森氏症患者，以及危及生命的心律失常。

The NIH/NCRR Research Resource for Complex Physiologic Signals was established in September. 1999, in order to accelerate research progress and to stimulate new basic and clinical studies of biomedical signals. This competitive ¿enewal request proposes to enhance and expand the components of this multidisciplinary Resource, including: 3hysioBank -- a large and growing archive of well-characterized digital recordings of physiologic signals and related data currently includes about 40 databases of cardiopulmonary, neural and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including sudden cardiac death, congestive heart failure, Parkinson's disease, sleep apnea, and aging. Core and collaborative research supporting this component develop new databases and specialized software to assist in this process. hysioToolkit -- a large and growing library of open source software for physiologic signal processing and analysis, detection of physiologically significant events using both classical techniques and novel methods based on statistical shysics and nonlinear dynamics, interactive display and characterization of signals, creation of new databases, simulation of physiologic and other signals, evaluation and comparison of analysis methods, and analysis of complex . orocesses. A unifying theme of research projects that contribute software to PhysioToolkit is the extraction of hidden information from biomedical signals, information that may have diagnostic or prognostic value in medicine, or explanatory or predictive power in basic research. PhysioNet -- the Resource's web site (www.physionet.org)provides free access to PhysioBank and PhysioToolkit, and facilities for discussion and cooperative analysis of data and .algorithms. PhysioNet offers a growing set of tutorials to assist in training investigators, clinicians and students in complex signal analysis. PhysioNet also hosts a highly successful series of open challenges that focus research efforts and promote rapid progress on important basic and clinical research questions. We also propose to develop new data analysis and archival biotechnology tools in support of the missions of the NCRR's General Clinical Research Centers, and to expand our core and collaborative research directed at a wide range of basic and clinical problems, including infant apnea and sleep disordered breathing, gait disorders and falls in elders and those with Parkinson's disease, and life-threatening cardiac arrhythmias.