Modeling peripheral arterial and venous pressure signals with integral pulse frequency modulation
Modeling peripheral arterial and venous pressure signals with integral pulse frequency modulation
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DOI：
10.1016/j.bspc.2023.105240
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发表时间：
2023-07-15
影响因子：
5.1
通讯作者：
Jensen，Morten O.
中科院分区：
文献类型：
--
作者：
Hayat，Abul;Wu，Jingxian;Jensen，Morten O.
本文提出了一种新的数学建模框架的外周动脉和外周静脉血压信号从猪的实验。可以在标准患者治疗期间使用常规导管采集外周血压信号。导管的微创性和无处不在的可用性使其成为各种应用的理想候选者。然而，在文献中没有外周血压信号的分析模型。我们解决这个问题，提出了一个模型，这些信号下的积分脉冲频率调制（IPFM）的框架。该模型结合了生理现象的影响，如心跳脉冲形状的变化，心率变异性，呼吸频率等模型参数通过应用IPFM模型从四头猪在不同的麻醉剂量下收集的实验数据。该模型可以拟合的实验数据与皮尔逊相关系数大于0.99和0.90的动脉和静脉血压信号，分别。两种不同麻醉分类的模型合成数据的性能与实验数据相当。脉冲形状和持续时间等参数也可以作为不同麻醉下的可区分特征。我们还提出了一种方法来区分麻醉诱导的心率变异性从其他原因。增加血管扩张麻醉剂的剂量类似于从脱水到水合。因此，在这项研究中获得的结果可以扩展在区分水合和脱水的对象。该模型可以扩展到类似的生物医学信号，如光电体积描记术，脑血流速度和多普勒波形。
This paper proposes a novel mathematical modeling framework for peripheral arterial and peripheral venous blood pressure signals from porcine experiments. Peripheral blood pressure signals can be acquired using regular catheters during standard patient treatment. The minimally invasive nature and ubiquitous availability of catheters render it an ideal candidate for various applications. However, there is no analytical model for peripheral blood pressure signals in the literature. We address this issue by proposing a model for these signals under the integral pulse frequency modulation (IPFM) framework. The model incorporates the impacts of physiological phenomena, such as the heartbeat pulse shape variation, heart rate variability, respiratory rate, etc. The model parameters are obtained by applying the IPFM model to experimental data collected from four pigs under different anesthetic dosages. The proposed model can fit the experimental data with Pearson correlation coefficients greater than 0.99 and 0.90 for arterial and venous blood pressure signals, respectively. The performance of model-synthesized data on the classification of two different anesthesia is comparable with experimental data. Parameters like pulse shape and duration can also work as distinguishable features under different anesthesia. We also proposed a way to distinguish respiratory-induced heart rate variability from other causes. Increasing doses of vasodilating anesthesia is similar to going from dehydration to hydration. Thus the results obtained in this study can be extended in distinguishing hydrated and dehydrated subjects. This model can be extended to similar biomedical signals like photoplethysmography, cerebral blood flow velocity, and Doppler waveforms.