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

Predicting neurological outcomes 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.

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.

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.

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.