Meste Olivier, Khaddoumi Balkine, Blain Grégory, Bermon Stéphane
Laboratory I3S, University of Nice and CNRS, Sophia Antipolis, France.
IEEE Trans Biomed Eng. 2005 Nov;52(11):1921-30. doi: 10.1109/TBME.2005.856257.
The analysis of heart period series is a difficult task especially under graded exercise conditions. From all the information present in these series, we are the most interested in the coupling between respiratory and cardiac systems, known as respiratory sinus arrythmia. In this paper, we show that precise patterns concerning the respiratory frequency can be extracted from the heart period series. An evolutive model is introduced in order to achieve tracking of the main respiratory-related frequencies and their time-varying amplitudes. Since respiration acts to modulate the sinus rhythm, we relate the frequencies and amplitudes to this modulation by analyzing in detail its nonlinear transformation giving the heart period signal. This analysis is performed assuming stationary conditions but also in the realistic case where the mean heart period, the amplitude, and the frequency of the respiration are time-varying. Since this paper is devoted to the theoretical and complete presentation of the method used in a physiological study published elsewhere, the capabilities of our method will be illustrated in a realistic simulated case.
对心动周期序列进行分析是一项艰巨的任务，尤其是在分级运动条件下。在这些序列中所呈现的所有信息中，我们最感兴趣的是呼吸和心脏系统之间的耦合，即呼吸性窦性心律失常。在本文中，我们表明可以从心动周期序列中提取有关呼吸频率的精确模式。引入了一个演化模型，以实现对主要呼吸相关频率及其随时间变化的幅度的跟踪。由于呼吸作用于调节窦性心律，我们通过详细分析其给出心动周期信号的非线性变换，将频率和幅度与这种调节联系起来。该分析是在假设平稳条件下进行的，但也考虑了实际情况，即平均心动周期、呼吸幅度和频率随时间变化的情况。由于本文致力于对在其他地方发表的一项生理学研究中所使用方法进行理论和完整的阐述，我们方法的能力将在一个实际模拟案例中得到说明。
