PhysioNet

Modern signal processing and machine learning methods have exciting potential to generate new knowledge that will impact both physiological understanding and clinical care. Access to data - particularly detailed clinical data - is often a bottleneck to progress. The overarching goal of PhysioNet is to accelerate research progress by freely providing rich archives of clinical and physiological data for analysis. The PhysioNet resource has three closely interdependent components: An extensive archive ("PhysioBank"), a large and growing library of software ("PhysioToolkit"), and a collection of popular tutorials and educational materials

现代信号处理与机器学习方法蕴含着激发新知的巨大潜力，这将深刻影响生理学理解和临床护理。数据的获取——尤其是详尽的临床数据——往往是进展的瓶颈。PhysioNet的总体目标是通过对临床和生理数据的丰富档案免费提供，以加速研究进程。PhysioNet资源包括三个紧密关联的组成部分：一个庞大的档案库（“PhysioBank”）、一个庞大且不断增长的软件库（“PhysioToolkit”），以及一系列流行的教程和教育材料。

搜集汇总

背景与挑战

背景概述

PhysioNet是一个提供丰富临床和生理数据的开放访问存储库，旨在通过共享数据加速医学和生命科学领域的研究。它包含PhysioBank数据存档、PhysioToolkit软件库及教育资源，由多个知名学术和医疗机构支持。