Biomedical Signal Interpretation, Computational Simulation (BSICoS) Group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Centro de Investigación Biomédica en Red (CIBER), Zaragoza, Spain.
Med Biol Eng Comput. 2018 May;56(5):781-794. doi: 10.1007/s11517-017-1724-9. Epub 2017 Sep 26.
Standard methodologies of heart rate variability analysis and physiological interpretation as a marker of autonomic nervous system condition have been largely published at rest, but not so much during exercise. A methodological framework for heart rate variability (HRV) analysis during exercise is proposed, which deals with the non-stationary nature of HRV during exercise, includes respiratory information, and identifies and corrects spectral components related to cardiolocomotor coupling (CC). This is applied to 23 male subjects who underwent different tests: maximal and submaximal, running and cycling; where the ECG, respiratory frequency and oxygen consumption were simultaneously recorded. High-frequency (HF) power results largely modified from estimations with the standard fixed band to those obtained with the proposed methodology. For medium and high levels of exercise and recovery, HF power results in a 20 to 40% increase. When cycling, HF power increases around 40% with respect to running, while CC power is around 20% stronger in running.
心率变异性分析的标准方法和作为自主神经系统状况标志物的生理解释在休息时已经得到了广泛的发表，但在运动时却没有那么多。本文提出了一种运动时心率变异性（HRV）分析的方法框架，该框架处理了运动时 HRV 的非平稳性质，包括呼吸信息，并识别和校正与心运动耦合（CC）相关的谱分量。该方法应用于 23 名男性受试者，他们进行了不同的测试：最大和次最大强度，跑步和骑车；同时记录心电图、呼吸频率和耗氧量。与标准固定带宽相比，高频（HF）功率的结果在很大程度上从估计值改为了采用所提出的方法获得的结果。在中高强度运动和恢复时，HF 功率增加了 20%到 40%。在骑车时，HF 功率相对于跑步增加了约 40%，而 CC 功率在跑步时则强约 20%。
