Centre de Recherche en Neuroscience de Lyon, Lyon, 69500, France
Centre de Recherche en Neuroscience de Lyon, Lyon, 69500, France.
eNeuro. 2023 Oct 27;10(10). doi: 10.1523/ENEURO.0197-23.2023. Print 2023 Oct.
Respiratory sinus arrhythmia (RSA), the natural variation in heart rate synchronized with respiration, has been extensively studied in emotional and cognitive contexts. Various time or frequency-based methods using the cardiac signal have been proposed to analyze RSA. In this study, we present a novel approach that combines respiratory phase and heart rate to enable a more detailed analysis of RSA and its dynamics throughout the respiratory cycle. To facilitate the application of this method, we have implemented it in an open-source Python toolbox called physio This toolbox includes essential functionalities for processing electrocardiogram (ECG) and respiratory signals, while also introducing this new approach for RSA analysis. Inspired by previous research conducted by our group, this method enables a cycle-by-cycle analysis of RSA providing the possibility to correlate any respiratory feature to any RSA feature. By employing this approach, we aim to gain a more accurate understanding of the neural mechanisms associated with RSA.
呼吸窦性心律失常（RSA）是心率随呼吸变化的自然波动，已在情绪和认知背景下得到广泛研究。各种基于时间或频率的方法都使用心脏信号来分析 RSA。在这项研究中，我们提出了一种新的方法，将呼吸相位和心率结合起来，以便更详细地分析 RSA 及其在整个呼吸周期中的动态变化。为了方便应用这种方法，我们在一个名为 physio 的开源 Python 工具包中实现了它。这个工具包包括处理心电图（ECG）和呼吸信号的基本功能，同时也引入了这种新的 RSA 分析方法。受我们小组之前研究的启发，这种方法可以对 RSA 进行逐周期分析，提供将任何呼吸特征与任何 RSA 特征相关联的可能性。通过采用这种方法，我们旨在更准确地了解与 RSA 相关的神经机制。
