The AI-HRV Sepsis Prediction Module is an advanced, real-time software that utilizes ECG-based heart rate variability (HRV) and machine learning to predict septic shock up to 120 minutes prior to its onset. The system extracts 10 HRV features—including time, frequency, and non-linear metrics—and applies an XGBoost model to estimate risk. It integrates with EHRs, delivers alerts, supports mobile notifications, and enables early, non-invasive, and scalable interventions in critical care.

This AI-driven system analyzes ECG-derived heart rate variability (HRV) to predict septic shock up to 120 minutes in advance. Using XGBoost, it continuously updates risk scores based on 10 HRV features every five minutes, identifying early autonomic dysfunction. Validated internally (AUROC 0.94) and on MIMIC-III (AUROC 0.78), it outperforms SOFA/qSOFA. The model is scalable to ICUs, EDs, wards, or home care, and adaptable for other acute or infectious diseases.

This AI-HRV system predicts septic shock up to 120 minutes in advance, enabling early intervention in emergency and ICU settings. It integrates seamlessly with existing ECG and hospital information systems, requiring no additional hardware. Its scalable, non-invasive design supports smart hospital deployment, remote monitoring, and commercialization. This innovation enhances critical care efficiency and holds global potential for sepsis management transformation.