Heart Rate Variability via Poincaré Mapping as an Early Biomarker Post-Cardiac Arrest

comes following cardiac arrest remains challenging. This study introduces a two-stage approach that combines a novel feature selection optimization with machine learning classification, utilizing heart rate variability (HRV) features for early and reliable prognostication. Methods: A rodent model resuscitated after a 7-min arrest was used. Features based on classic HRV and advanced Poincaré vector mapping were extracted. An Ant Colony Optimization method with Dynamic Pheromone Decay and Knowledge Distillation (ACO-DPKD) was employed for efficient feature optimization due to its ability to adaptively prioritize complex feature interactions. Selected features were classified using a support vector machine. Results: ACO-DPKD identified key HRV features, enabling accurate prediction of neurological outcomes within 1 hour of resuscitation, achieving 90% accuracy. Integration of advanced Poincaré metrics with traditional HRV features improved prediction accuracy by approximately 20%, underscoring their clinical relevance for early neurological assessment. Significance: Optimized classification within the critical first hour after cardiac arrest lays the foundation for timely neuroprotective interventions, with advanced Poincaré vector features playing a major role in driving early prognostic accuracy.

Heart rate variability (HRV), Poincaré plot, cardiac arrest, autonomic nervous system, neurological outcome, biomarker.

Background: