Saeed M, Mark R G
Harvard-MIT, Cambridge, USA.
Comput Cardiol. 2000;27:797-800.
An Intelligent Patient Monitoring (IPM) framework is defined for the analysis and display of multiparameter trends from ICU patients. Wavelet analysis was utilized for detection of physiological events and artifacts in long-term trends. A group of 58 patients from the MIMIC database were identified in which the heart rate (HR), arterial blood pressure (ABP), and pulmonary artery pressure (PAP) were monitored. An estimated cardiac output (CO) signal, using HR and ABP, was shown to correlate strongly (r=.67) with actual CO measurements. Using wavelet analysis, automated artifact and physiological event detection algorithms were developed to monitor left ventricular hemodynamic function. Finally, an intelligent display system is presented for presentation of the data in ICU monitors.
定义了一种智能患者监测（IPM）框架，用于分析和显示重症监护病房（ICU）患者的多参数趋势。小波分析用于检测长期趋势中的生理事件和伪迹。从多参数智能监测数据库（MIMIC）中识别出一组58名患者，对其心率（HR）、动脉血压（ABP）和肺动脉压（PAP）进行监测。利用心率和动脉血压得出的估计心输出量（CO）信号与实际心输出量测量值具有很强的相关性（r = 0.67）。使用小波分析开发了自动伪迹和生理事件检测算法，以监测左心室血流动力学功能。最后，展示了一种智能显示系统，用于在ICU监护仪中呈现数据。
