Aboy Mateo, McNames James, Thong Tran, Tsunami Daniel, Ellenby Miles S, Goldstein Brahm
Electronics Engineering Technology Department, Oregon Institute of Technology, Portland, OR 97229, USA.
IEEE Trans Biomed Eng. 2005 Oct;52(10):1662-70. doi: 10.1109/TBME.2005.855725.
Beat detection algorithms have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop their own algorithms. We designed an automatic detection algorithm for pressure signals that locates the first peak following each heart beat. This is called the percussion peak in intracranial pressure (ICP) signals and the systolic peak in arterial blood pressure (ABP) and pulse oximetry (SpO2) signals. The algorithm incorporates a filter bank with variable cutoff frequencies, spectral estimates of the heart rate, rank-order nonlinear filters, and decision logic. We prospectively measured the performance of the algorithm compared to expert annotations of ICP, ABP, and SpO2 signals acquired from pediatric intensive care unit patients. The algorithm achieved a sensitivity of 99.36% and positive predictivity of 98.43% on a dataset consisting of 42,539 beats.
搏动检测算法有许多临床应用，包括脉搏血氧饱和度测定、心律失常检测和心输出量监测。这些算法大多由医疗设备公司开发，属于专有算法。因此，希望研究脉搏轮廓分析的研究人员必须依靠手动标注或自行开发算法。我们设计了一种用于压力信号的自动检测算法，该算法可定位每次心跳后的第一个峰值。在颅内压（ICP）信号中，这个峰值称为叩击峰；在动脉血压（ABP）和脉搏血氧饱和度（SpO2）信号中，这个峰值称为收缩峰。该算法包含一个具有可变截止频率的滤波器组、心率的频谱估计、排序非线性滤波器和决策逻辑。我们前瞻性地测量了该算法的性能，并与从儿科重症监护病房患者采集的ICP、ABP和SpO2信号的专家标注结果进行了比较。在一个包含42539次搏动的数据集上，该算法的灵敏度达到了99.36%，阳性预测值达到了98.43%。
