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A Simplified Two-Component Model of Blood Pressure Fluctuation

机译:血压波动的简化的两成分模型

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摘要

We propose a simple moving-average (MA) model that uses the low-frequency (LF) component of the peroneal muscle sympathetic nerve spike rate (LFspike rate) and the high-frequency (HF) component of respiration (HFResp) to describe the LF neurovascular fluctuations and the HF mechanical oscillations in systolic blood pressure (SBP), respectively. This method was validated by data from eight healthy subjects (23–47 yr old, 6 male, 2 female) during a graded tilt (15° increments every 5 min to a 60° angle). The LF component of SBP (LFSBP) had a strong baroreflex-mediated feedback correlation with LFspike rate (r = −0.69 ± 0.05) and also a strong feedforward relation to LFspike rate [r = 0.58 ± 0.03 with LFSBP delay (τ) = 5.625 ± 0.15 s]. The HF components of spike rate (HFspike rate) and SBP (HFSBP) were not significantly correlated. Conversely, HFResp and HFSBP were highly correlated (r = −0.79 ± 0.04), whereas LFResp and LFSBP were significantly less correlated (r = 0.45 ± 0.08). The mean correlation coefficients between the measured and model-predicted LFSBP (r = 0.74 ± 0.03) in the supine position did not change significantly during tilt. The mean correlation between the measured and model-predicted HFSBP was 0.89 ± 0.02 in the supine position. R2 values for the regression analysis of the model-predicted and measured LF and HF powers indicate that 78 and 91% of the variability in power can be explained by the linear relation of LFspike rate to LFSBP and HFResp to HFSBP. We report a simple two-component model using neural sympathetic and mechanical respiratory inputs that can explain the majority of blood pressure fluctuation at rest and during orthostatic stress in healthy subjects.
机译:我们提出了一个简单的移动平均(MA)模型,该模型使用腓骨肌肉交感神经尖峰频率(LFspike rate)的低频(LF)分量和呼吸频率(HFResp)的高频(HF)分量来描述LF的神经血管波动和收缩压(SBP)的HF机械振荡。该方法已通过八名健康受试者(23-47岁,6名男性,2名女性)在倾斜度(每5分钟以15°递增至60°角)期间的数据进行验证。 SBP的LF分量(LFSBP)与LFspike速率具有很强的压力反射介导的反馈相关性(r = -0.69±0.05),并且与Lspike速率也具有很强的前馈关系[r = 0.58±0.03,LFSBP延迟(τ)= 5.625 ±0.15 s]。刺突率(HFspike rate)和SBP(HFSBP)的HF成分没有显着相关。相反,HFResp和HFSBP的相关性很高(r = -0.79±0.04),而LFResp和LFSBP的相关性则较低(r = 0.45±0.08)。在倾斜过程中,仰卧位的实测和模型预测的LFSBP(r = 0.74±0.03)之间的平均相关系数没有明显变化。仰卧位测量的HFSBP与模型预测的HFSBP之间的平均相关性为0.89±0.02。用于模型预测和测量的LF和HF功率的回归分析的R 2 值表明,功率的可变性的78%和91%可以通过LF峰值频率与LFSBP和HF的线性关系来解释 Res 到HF SBP 。我们报告了一个使用神经交感和机械呼吸输入的简单的两成分模型,该模型可以解释健康受试者静息时和体位压力时的大多数血压波动。

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