Estimation of Breathing Rate from Respiratory Sinus Arrhythmia: Comparison of Various Methods

Although respiratory sinus arrhythmia (RSA) is a well-known and often studied phenomenon, methods to estimate (average) respiratory rate from heart rate variability via RSA have been investigated and published only sparsely. We reinvestigate three published techniques and contrast them to our own approaches. All methods were also evaluated for respiration signals to yield approximations of the true breathing rate for comparison. Our analyses are based on physiological recordings available at PhysioNet, an online database. Results show that the RSA of young supine subjects yields good approximations of mean respiratory rate in the case of time series longer than 1 min, while the estimations become noticeably less accurate for elderly persons. Our own “advanced counting method” produced the best results, and in addition principally permits even the definition of instantaneous respiratory rates. Consequently, it is recommended for further investigations.

Using inverse electrocardiography to image myocardial infarction--reflecting on the 2007 PhysioNet/Computers in Cardiology Challenge

The goal of the 2007 PhysioNet/Computers in Cardiology Challenge was to try to establish how well it is possible to characterize the location and extent of old myocardial infarcts using electrocardiographic evidence supplemented by anatomical imaging information. A brief overview of the challenge and how different challengers approached the competition is provided, followed by detailed response of the first author to integrate electrophysiologic and anatomical data. The first author used the provided 120-electrode body-surface potential mapping data and magnetic resonance imaging heart and torso images to calculate epicardial potentials on customized ventricular geometries. A method was developed to define the location and extent of scar tissue based on the morphology of computed epicardial electrograms. Negative Q-wave deflection followed by R-wave on the left ventricular surface seemed to correspond with the location of the scar as determined by the gadolinium-enhanced magnetic resonance imaging gold standard in the supplied data sets. The method shows promising results as a noninvasive imaging tool to quantitatively characterize chronic infarcts and warrants further investigation on a larger patient data set.     

复杂生理信号研究资源-PhysioNet及其在抑制监护仪错误报警中的应用开发

目的:研究复杂生理信号研究资源-PhysioNet的应用开发方法,研究抑制监护仪错误报警的算法.方法:首先详细介绍了PhyrsioNet以及它的三个相互关联的组成部分:数据库PhysioBank,软件库PhysioToolkit和网络资源平台PhysioNet;其次,研究了应用PhysioNet进行算法开发研究的方法,然后研究了抑制监护仪错误报警的算法,利用PhysioToolkit软件包开发程序,取PhysioBank里的MIMIC Ⅱ数据库中的心动过缓和心动过速报警数据进行分析,分类检测了监护仪产生的报警信息,对算法的性能进行了整体评价.结果:算法对真实报警的正确识别率为99.64%,对错误报警的抑制率为66.18%.结论:PhysioNet是分析人体复杂生理信号的重要的数据资源和研究开发平台,我们将其应用开发,对抑制监护仪错误报警算法的性能进行了整体评价,取得了较好的效果.     

PhysioNet信息资源解析及利用

介绍基于Web的复杂生理信号和生物医学信号研究资源网站PhysioNet及其相互关联的组成部分：数据库PhysioBank,软件库PhysioToolkit和网络资源平台PhysioNet。探讨医学科研人员利用PhysioNet进行算法研究的过程,并对算法的性能进行整体评价。