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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 behavioral 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 in 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 Poincare plot descriptors of interbeat intervals from the electrocardiogram. To validate our method, we used electrocardiograms gathered from different 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, which are mostly triggered by sympathetic and parasympathetic activity, respectively. Our method constitutes a valuable, robust, time-resolved, and cost-effective tool for a better understanding of autonomic activity through HR and HRV in healthy state and potentially for pathological conditions.