Suppr超能文献

心血管功能与心冲击图:通过数学建模解读的关系。

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling.

出版信息

IEEE Trans Biomed Eng. 2019 Oct;66(10):2906-2917. doi: 10.1109/TBME.2019.2897952. Epub 2019 Feb 6.

Abstract

OBJECTIVE

To develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG).

METHODS

A closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed.

RESULTS

Simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M, and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition.

CONCLUSION

The proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology.

SIGNIFICANCE

This paper provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present paper considers an average human body and can potentially be extended to include variability among individuals.

摘要

目的

开发用于临床解读心冲击图(BCG)的定量方法。

方法

提出了一个心血管系统的闭环数学模型,从理论上模拟产生 BCG 信号的机制,然后将其与悬浮床上加速度计获取的信号进行比较。

结果

模拟的动脉压力波形和心室功能与临床文献中报道的情况在定性和定量上都非常吻合。模拟的 BCG 信号显示出典型的 I、J、K、L、M 和 N 峰,与实验测量结果具有良好的定性和定量一致性。与左心室收缩力降低和僵硬度增加相关的模拟 BCG 信号显示出特定病理状态的特征性变化。

结论

所提出的闭环模型捕捉到了 BCG 信号的主要特征,并能够根据心血管生理学的基本机制预测病理变化。

意义

本文为 BCG 信号的临床解读和 BCG 传感设备的优化提供了一个定量框架。本文考虑了一个平均人体,并且有可能扩展到包括个体之间的差异。

相似文献

引用本文的文献

本文引用的文献

1
Sleep Posture Classification Using Bed Sensor Data and Neural Networks.利用床传感器数据和神经网络进行睡眠姿势分类
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:461-465. doi: 10.1109/EMBC.2018.8512436.
2
Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System.使用液压床传感器系统监测相对血压。
IEEE Trans Biomed Eng. 2019 Mar;66(3):740-748. doi: 10.1109/TBME.2018.2855639. Epub 2018 Jul 13.
6
Sleep stage recognition using respiration signal.利用呼吸信号进行睡眠阶段识别。
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2843-2846. doi: 10.1109/EMBC.2016.7591322.
10
Deep vein thrombosis and pulmonary embolism.深静脉血栓形成和肺栓塞。
Lancet. 2016 Dec 17;388(10063):3060-3073. doi: 10.1016/S0140-6736(16)30514-1. Epub 2016 Jun 30.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验