清醒大鼠动脉血压信号的预处理及谱分析

目的:研究模拟失重大鼠清醒状态下收缩压与心率变异性的信号预处理及谱分析方法. 方法:以尾部悬吊大鼠模型模拟失重对心血管的影响,通过股动脉插管术在清醒状态下进行血压记录. 数据分析使用血压变异性检测算法获取收缩压变异性数据,并从中提取PP间期序列用以替代RR间期数据;使用周期图法对SBPV进行谱分析. 结果:一阶导数阈值与模板匹配检测算法具有较高的检测准确率;从SBPV数据中提取的PP间期能够作为心电图RR间期的替代数据;通过对SBPV信号进行周期图谱估计可将血压波动信号分解为高频、低频与极低频三个不同频率范围的周期波动. 结论:使用周期图谱估计能够较好揭示模拟失重大鼠血压变化的特征,在利用大鼠进行重力心血管研究中有一定应用价值.

Pre-processing and spectral analysis of arterial blood pressure signals in conscious rats

AIM: To apply pre-processing and spectral analysis of systolic blood pressure and heart rate signals in conscious rats to evaluate changes in cardiovascular regulatory function induced by simulated weightlessness. METHODS: The tail-suspended, hindlimb-unloaded (HU) rat model was used to simulate the cardiovascular effect of microgravity. A pressure transducer connected to a PE-50-PE-10 catheter was inserted via the right femoral artery into the posterior abdominal aorta in conscious rats. We applied first derivative and template match algorithm to obtain systolic blood pressure variability (SBPV) data form original blood pressure data. PP time series extracted form the SBP time series were used to substitute the data of RR time series. SBPV data were analyzed by periodogram. RESULTS: An algorithm of first derivative and template match was suitable for effectively identifying SBP time series. The PP time series could replace RR time series. Power spectrum of SBPV, estimated by periodogram could be divided into 3 different frequency bands, very low frequency, low frequency, and high frequency. CONCLUSION: Periodogram is a useful method for blood pressure data processing, which is effective in detecting cardiovascular dysfunction in conscious rats after a 14-d simulated microgravity, and may be helpful for cardiovascular signal analysis in conscious rats.