The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Equipment Management and Support College, Engineering University of People's Armed Police, Xi'an, China.
J Appl Physiol (1985). 2019 Aug 1;127(2):320-327. doi: 10.1152/japplphysiol.00125.2019. Epub 2019 Jun 20.
Frequency domain analysis of heart rate variability (HRV) is a noninvasive method to evaluate the autonomic nervous system (ANS), but the traditional parameters of HRV, i.e., the power spectra of the high-frequency (HF) and low-frequency bands (LF), cannot estimate the activity of the parasympathetic (PNS) and sympathetic nervous systems (SNS) well. The aim of our study was to provide a corrected method to better distinguish the contributions of the PNS and SNS in the HRV spectrum. Respiration has a gating effect on cardiac vagal efferent activity, which induces respiration-locked heart rate (HR) changes because of the fast effect of the PNS. So the respiration-related heart rate (HR) is closely related to PNS activity. In this study, HR was decomposed into HR and the respiration-unrelated component (HR) based on empirical mode decomposition (EMD) and the relationship between HR and respiration. Time-frequency analysis of HR and HR was defined as HF and LF, respectively, with specific adaptive bands for every signal. Two experimental data sets, representing SNS and PNS activation, respectively, were used for efficiency analysis of our method. Our results show that the corrected HRV predicted ANS activity well. HF could be an index of PNS activity, LF mainly reflected SNS activity, and LF/HF could be more accurate in representing the sympathovagal balance. This study includes the time-varying relationship between respiration and heart rate in the analysis of heart rate variability. Correction for low-frequency and high-frequency components based on respiration significantly improved evaluation of the sympathetic and parasympathetic nervous systems.
心率变异性（HRV）的频域分析是评估自主神经系统（ANS）的一种非侵入性方法，但 HRV 的传统参数，即高频（HF）和低频带（LF）的功率谱，不能很好地估计副交感神经系统（PNS）和交感神经系统（SNS）的活动。我们的研究目的是提供一种校正方法，以更好地区分 HRV 谱中 PNS 和 SNS 的贡献。呼吸对心脏迷走神经传出活动有门控作用，这会由于 PNS 的快速作用而引起呼吸锁定的心率（HR）变化，因此与呼吸相关的心率（HR）与 PNS 活动密切相关。在这项研究中，HR 基于经验模态分解（EMD）和 HR 与呼吸之间的关系，被分解为 HR 和与呼吸无关的成分（HR）。HR 和 HR 的时频分析分别定义为 HF 和 LF，每个信号都有特定的自适应带宽。使用分别代表 SNS 和 PNS 激活的两个实验数据集来分析我们方法的效率。我们的结果表明，校正后的 HRV 很好地预测了 ANS 活动。HF 可以作为 PNS 活动的指标，LF 主要反映 SNS 活动，LF/HF 可以更准确地表示交感神经和副交感神经的平衡。这项研究包括了在心率变异性分析中呼吸和心率之间的时变关系。基于呼吸对低频和高频分量的校正显著提高了对交感和副交感神经系统的评估。
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