Work

Predicting neurological recovery following cardiac arrest based on dynamic brain–heart coupling

Year: 2026

Type: article

Abstract: BACKGROUND: Cardiac arrest (CA) often results in severe neurological injury, with both the central (CNS) and autonomic nervous systems (ANS) playing critical roles in recovery. Brain–heart coupling (BHC) and heartbeat-evoked potentials (HEP) reflect CNS and ANS interactions, yet their temporal evolution after return of spontaneous circulation (ROSC) and prognostic relevance remain unclear. OBJECTIVE: This study aims to examine frequency-specific variations in BHC and HEP within the first 96 h after ROSC in cardiac arrest patients and evaluate their prognostic value for neurological recovery. METHODS: We analyzed physiological data from 277 CA patients, focusing on BHC and HEP metrics. Unlike previous studies, hourly analyses were performed for each patient in order to investigate the temporal evolution of BHC and HEP following cardiac arrest. EEG and ECG data were preprocessed, and the Poincaré Sympathetic-Vagal Synthetic Data Generation (PSV-SDG) model was used to compute the strength. Refined Composite Multiscale Entropy (RCMSE) was applied to assess BHC complexity. Additionally, HEP was further examined for spatiotemporal features. Logistic regression (LR) and support vector machine (SVM) models were applied to predict 3-month outcome using BHC and HEP features at multiple post-CA time points to assess their prognostic value over time. RESULTS: We found that patients with good outcome demonstrated significantly higher BHC strength and complexity, particularly in the delta and theta bands, with notable increases between 38–47 h post-ROSC. HEP analysis showed enhanced positive peaks and widespread cortical activation in this group, suggesting more robust cortical-autonomic integration. In contrast, the poor outcome group exhibited weaker, more localized BHC and unstable HEP responses. Machine learning models combining BHC and HEP features achieved strong prognostic performance, with the highest AUC of 0.98 observed at 70 h post-ROSC, identifying it as the most informative time point for outcome prediction. CONCLUSION: This study demonstrates that BHC and HEP metrics offer important insights into post-CA recovery. Notably, measures obtained at 70 h post-ROSC provided the greatest prognostic value, highlighting their potential to inform early clinical decision-making in critical care.

Source: Journal of NeuroEngineering and Rehabilitation

DOI: 10.1186/s12984-026-01971-2