Heart Rate Variability and Autonomic Dysfunction After Stroke: Prognostic Markers for Recovery

Stroke is a major cause of long-term disability and mortality worldwide, often resulting in impairments not only in motor and cognitive functions but also in autonomic nervous system (ANS) regulation. Among the physiological markers that reflect ANS activity, heart rate variability (HRV) has emerged as a promising biomarker for assessing stroke severity and predicting recovery outcomes. HRV quantifies the temporal fluctuations between heartbeats and is traditionally analyzed through time- and frequency-domain measures. More recent approaches have introduced non-linear metrics such as approximate entropy, sample entropy, and detrended fluctuation analysis to capture complex heart rate dynamics. In this narrative review, we address the role of both linear and non-linear HRV parameters in the context of stroke, highlighting their relevance for understanding autonomic dysfunction and guiding rehabilitation. Evidence shows that reduced HRV is associated with poorer functional outcomes, higher mortality, and increased risk of complications post-stroke. Moreover, HRV trends can provide valuable insights into treatment effectiveness and individual recovery trajectories. We also discuss practical considerations for HRV measurement, including device selection, preprocessing strategies, and the need for methodological standardization. Finally, we outline interventional strategies that may enhance HRV and promote better recovery. Together, these findings support the integration of HRV analysis into stroke care as a non-invasive, accessible tool to guide prognosis and tailor interventions.