A Plateau wave (PW) represents a distinctive pattern of Intracranial Pressure (ICP) change observed in patients with severe traumatic brain injuries (TBI), marked by a sudden sustained increase in ICP. These pathological stress events are frequently linked with significant alterations in Heart Rate Variability (HRV), indicative of Autonomic Nervous System (ANS) dysfunction. This study aims to investigate the coupling between ICP and HRV by employing the Mutual Information Rate (MIR). MIR serves as an extension of Mutual Information (MI), enabling the analysis of the dynamic exchange of information across various time intervals. Furthermore, the MIR between two random processes can be decomposed into distinct entropy rate components associated with the concept of complexity, as well as conditional mutual information terms related to information transfer. The MIR and its constituent information terms are estimated through a model-free approach based on the nearest neighbors search (KNN). This framework is first validated on simulations of linear and non-linear bivariate systems, then it is applied to data consisting of RR intervals and ICP amplitude (AMP) time series measured in TBI patients with PW occurrence. The obtained results evidence that MIR decompositions are able to highlight the interdependence of HRV and ICP in PW episodes and the association of these critical phenomena with autonomic stress.