School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, United Kingdom. School of Cardiovascular Medicine and Sciences, Faculty of Life Sciences and Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, United Kingdom.
Physiol Meas. 2018 Oct 30;39(10):104008. doi: 10.1088/1361-6579/aae46a.
Current arterial pulse monitoring systems capture data at high frequencies (100-1000 Hz). However, they typically report averaged or low frequency summary data such as heart rate and systolic, mean and diastolic blood pressure. In doing so, a potential wealth of information contained in the high-fidelity waveform data is discarded, data which has long been known to contain useful information on cardiovascular performance. Here we summarise a new mathematical method, attractor reconstruction, which enables the quantification of arterial waveform shape and variability in real-time. The method can handle long streams of non-stationary data and does not require preprocessing of the raw physiological data by the end user. Whilst the detailed mathematical proofs have been described elsewhere (Aston et al 2008 Physiol. Meas. 39), the authors were motivated to write a summary of the method and its potential utility for biomedical researchers, physiologists and clinician readers. Here we illustrate how this new method may supplement and potentially enhance the sensitivity of detecting cardiovascular disturbances, to aid with biomedical research and clinical decision making.
目前的动脉脉搏监测系统以高频率(100-1000Hz)采集数据。然而,它们通常报告平均或低频的汇总数据,如心率、收缩压、平均压和舒张压。这样,就丢弃了在高保真波形数据中包含的大量信息,而这些信息长期以来一直被认为包含有关心血管性能的有用信息。在这里,我们总结了一种新的数学方法,即吸引子重建,它可以实时量化动脉波形的形状和可变性。该方法可以处理长时间的非平稳数据流,并且不需要用户对原始生理数据进行预处理。虽然详细的数学证明已经在其他地方描述过(Aston 等人,2008 年,《生理学测量》39),但作者还是想为生物医学研究人员、生理学家和临床医生读者写一篇关于该方法及其潜在应用的概述。在这里,我们举例说明了这种新方法如何补充和可能增强检测心血管干扰的敏感性,以帮助生物医学研究和临床决策。