Ellis Tim, McNames James, Aboy Mateo
Biomedical Signal Processing Laboratory, Electrical and Computer Engineering Department, Portland State University, 1900 SW 4th Ave., Portland, OR 97201, USA.
IEEE Trans Biomed Eng. 2007 Sep;54(9):1552-9. doi: 10.1109/TBME.2007.892918.
We present a new analysis and visualization method for studying the functional relationship between the pulse morphology of pressure signals and time or signal metrics such as heart rate, pulse pressure, and means of pressure signals, such as arterial blood pressure and central venous pressure. The pulse morphology is known to contain potentially useful clinical information, but it is difficult to study in the time domain without the aid of a tool such as the method we present here. The primary components of the method are established signal processing techniques, nonparametric regression, and an automatic beat detection algorithm. Some of the insights that can be gained from this are demonstrated through the analysis of intracranial pressure signals acquired from patients with traumatic brain injuries. The analysis indicates the point of transition from low-pressure morphology consisting of three distinct peaks to a high-pressure morphology consisting of a single peak. In addition, we demonstrate how the analysis can reveal distinctions in the relationship between morphology and several signal metrics for different patients.
我们提出了一种新的分析和可视化方法，用于研究压力信号的脉搏形态与时间或信号指标（如心率、脉压以及动脉血压和中心静脉压等压力信号的均值）之间的函数关系。已知脉搏形态包含潜在有用的临床信息，但如果没有我们在此介绍的这种方法等工具的辅助，在时域中进行研究就很困难。该方法的主要组成部分是既定的信号处理技术、非参数回归和自动搏动检测算法。通过对从创伤性脑损伤患者获取的颅内压信号进行分析，展示了可以从中获得的一些见解。分析表明了从由三个不同峰值组成的低压形态到由单个峰值组成的高压形态的转变点。此外，我们还展示了该分析如何揭示不同患者在形态与几个信号指标之间关系上的差异。
