Candia-Rivera Diego, de Vico Fallani Fabrizio, Chavez Mario
Sorbonne Université, Paris Brain Institute (ICM), CNRS UMR7225, INRIA Paris, INSERM U1127, Hôpital de la Pitié Salpêtrière, AP-HP, Paris 75013, France.
R Soc Open Sci. 2025 Jan 15;12(1):240750. doi: 10.1098/rsos.240750. eCollection 2025 Jan.
The time-resolved analysis of heart rate (HR) and heart rate variability (HRV) is crucial for the evaluation of the dynamic changes of autonomic activity under different clinical and behavioural conditions. Standard HRV analysis is performed in the frequency domain because the sympathetic activations tend to increase low-frequency HRV oscillations, while the parasympathetic ones increase high-frequency HRV oscillations. However, a strict separation of HRV into frequency bands may cause biased estimations, especially in the low-frequency range. To overcome this limitation, we propose a robust estimator that combines HR and HRV dynamics, based on the correlation of the Poincaré plot descriptors of interbeat intervals from the electrocardiogram. To validate our method, we used electrocardiograms gathered from open databases where standardized paradigms were applied to elicit changes in autonomic activity. Our proposal outperforms the standard spectral approach for the estimation of low- and high-frequency fluctuations in HRV, and its performance is comparable with newer methods. Our method constitutes a valuable, robust, time-resolved and cost-effective tool for a better understanding of autonomic activity through HR and HRV in a healthy state and potentially for pathological conditions.
心率（HR）和心率变异性（HRV）的时间分辨分析对于评估不同临床和行为条件下自主神经活动的动态变化至关重要。标准的HRV分析是在频域中进行的，因为交感神经激活往往会增加低频HRV振荡，而副交感神经激活则会增加高频HRV振荡。然而，将HRV严格划分为不同频段可能会导致有偏差的估计，尤其是在低频范围内。为了克服这一局限性，我们基于心电图中逐搏间期的庞加莱图描述符的相关性，提出了一种结合HR和HRV动态的稳健估计器。为了验证我们的方法，我们使用了从开放数据库收集的心电图，在这些数据库中应用了标准化范式来引发自主神经活动的变化。我们的方法在估计HRV的低频和高频波动方面优于标准频谱方法，其性能与更新的方法相当。我们的方法是一种有价值、稳健、具有时间分辨能力且经济高效的工具，有助于在健康状态下以及潜在的病理状态下，通过HR和HRV更好地理解自主神经活动。
