Xu Peng, Bergsneider Marvin, Hu Xiao
Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, Los Angeles, United States.
Med Eng Phys. 2009 Apr;31(3):337-45. doi: 10.1016/j.medengphy.2008.06.005. Epub 2008 Jul 15.
Detecting onsets of cardiovascular pulse wave signals is an important prerequisite for successfully conducting various analysis tasks involving the concept of pulse wave velocity. However, pulse onsets are frequently influenced by inherent noise and artifacts in signals continuously acquired in a clinical environment. The present work proposed and validated a neighbor pulse-based signal enhancement algorithm for reducing error in the detected pulse onset locations from noise-contaminated pulsatile signals. Pulse onset was proposed to be detected using the first principal component extracted from three adjacent pulses. This algorithm was evaluated using test signals constructed by mixing arterial blood pressure, cerebral blood flow velocity and intracranial pressure pulses recorded from neurosurgical patients with white noise of various levels. The results showed that the proposed pulse enhancement algorithm improved (p<0.05) pulse onset detection according to all three different onset definitions and for all three types of pulsatile signals as compared to results without using the pulse enhancement. These results suggested that the proposed algorithm could help achieve robustness in pulse onset detection and facilitate pulse wave analysis using clinical recordings.
检测心血管脉搏波信号的起始点是成功开展涉及脉搏波速度概念的各种分析任务的重要前提。然而，在临床环境中持续采集的信号中，脉搏起始点经常受到固有噪声和伪迹的影响。目前的工作提出并验证了一种基于相邻脉搏的信号增强算法，以减少噪声污染的搏动信号中检测到的脉搏起始点位置的误差。建议使用从三个相邻脉搏中提取的第一主成分来检测脉搏起始点。使用通过将神经外科患者记录的动脉血压、脑血流速度和颅内压脉搏与不同水平的白噪声混合构建的测试信号对该算法进行了评估。结果表明，与不使用脉搏增强的结果相比，所提出的脉搏增强算法根据所有三种不同的起始点定义以及所有三种类型的搏动信号，均改善了（p<0.05）脉搏起始点检测。这些结果表明，所提出的算法有助于实现脉搏起始点检测的稳健性，并便于使用临床记录进行脉搏波分析。
