Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.
Department of Computer Engineering, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.
Physiol Meas. 2024 Sep 16;45(9). doi: 10.1088/1361-6579/ad7778.
The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS).The analysed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5 min sessions of controlled breathing at three different frequencies: 6, 10 and 15 breaths min. COconcentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including phase-rectified signal averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy, approximate entropy, sample entropy, and fuzzy entropy, as well as time and frequency-domain measures (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV).Higher breathing rates resulted in a significant decrease in end-tidal COconcentration (< 0.001), accompanied by increases in both ABP (0.001) and heart rate (HR,0.001). A strong, linear decline in AC and DC (0.001 for both) was observed with increasing breathing rate. All entropy metrics increased with breathing frequency (0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (= 0.038 and= 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies.Alterations in COlevels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations.
本研究旨在探讨呼吸刺激如何影响自主神经系统（ANS）的非线性和线性指标。分析数据集包含 70 名年轻健康的志愿者，在 5 分钟的受控呼吸期间，通过非侵入性方式测量他们的动脉血压（ABP），呼吸频率分别为 6、10 和 15 次/分钟。在整个受控呼吸期间，连续监测 CO 浓度和呼吸频率。使用非线性方法对 ANS 进行了特征描述，包括相位整流信号平均（PRSA）来估计心率加速度和减速能力（AC、DC）、多尺度熵、近似熵、样本熵和模糊熵，以及心率变异性（HRV）的时频域测量（低频，LF；高频，HF；总功率，TP）。较高的呼吸频率导致呼气末 CO 浓度显著下降（<0.001），同时 ABP（0.001）和心率（HR，0.001）增加。随着呼吸频率的增加，AC 和 DC 呈明显的线性下降（两者均为 0.001）。所有的熵指标都随呼吸频率的增加而增加（均为 0.001）。在时域中，HRV 指标随呼吸频率显著下降（均为 0.01）。在频域中，HRV LF 和 HRV HF 降低（分别为=0.038 和=0.040），尽管这些变化较小。HRV TP 随呼吸频率无显著变化。CO 水平的变化、强有力的化学感受器触发以及心率的变化很可能调节 ANS 指标。与依赖频率的 HRV 指标相比，非线性 PRSA 和熵似乎对呼吸刺激更为敏感。需要进一步研究，以验证我们的观察结果，需要涉及更大样本量的健康受试者。
