U.S. Army Institute of Surgical Research, 3650 Chambers Pass, Building 3610, Fort Sam Houston, TX, 78234-6315, USA,
J Clin Monit Comput. 2014 Apr;28(2):123-31. doi: 10.1007/s10877-013-9503-0. Epub 2013 Aug 30.
Heart-rate complexity (HRC) has been proposed as a new vital sign for critical care medicine. The purpose of this research was to develop a reliable method for determining HRC continuously in real time in critically ill patients using multiple waveform channels that also compensates for noisy and unreliable data. Using simultaneously acquired electrocardiogram (Leads I, II, V) and arterial blood pressure waveforms sampled at 360 Hz from 250 patients (over 375 h of patient data), we evaluated a new data fusion framework for computing HRC in real time. The framework employs two algorithms as well as signal quality indices. HRC was calculated (via the method of sample entropy), and equivalence tests were then performed. Bland-Altman plots and box plots of differences between mean HRC values were also obtained. Finally, HRC differences were analyzed by paired t tests. The gold standard for obtaining true means was manual verification of R waves and subsequent entropy calculations. Equivalence tests between mean HRC values derived from manually verified sequences and those derived from automatically detected peaks showed that the "Fusion" values were the least statistically different from the gold standard. Furthermore, the fusion of waveform sources produced better error density distributions than those derived from individual waveforms. The data fusion framework was shown to provide in real-time a reliable continuously streamed HRC value, derived from multiple waveforms in the presence of noise and artifacts. This approach will be validated and tested for assessment of HRC in critically ill patients.
心率复杂度（HRC）已被提议作为重症监护医学的一种新的生命体征。本研究的目的是开发一种可靠的方法，使用多波形通道实时连续地确定危重病患者的 HRC，同时补偿嘈杂和不可靠的数据。使用同时采集的心电图（导联 I、II、V）和动脉血压波形（来自 250 名患者的 360Hz 样本，超过 375 小时的患者数据），我们评估了一种新的数据融合框架，用于实时计算 HRC。该框架采用了两种算法以及信号质量指标。通过样本熵法计算 HRC，然后进行等效性检验。还获得了平均 HRC 值之间差异的 Bland-Altman 图和箱线图。最后，通过配对 t 检验分析 HRC 差异。获得真实平均值的金标准是手动验证 R 波和随后的熵计算。手动验证序列和自动检测峰值得出的平均 HRC 值之间的等效性检验表明，“融合”值与金标准的统计差异最小。此外，波形源的融合产生的误差密度分布优于单个波形的误差密度分布。数据融合框架被证明能够在存在噪声和伪影的情况下，从多个波形实时提供可靠的连续流式 HRC 值。这种方法将得到验证和测试，用于评估危重病患者的 HRC。
