An algorithmic framework for real-time multi-parameter fusion of biometric data and anomaly detection in cardiac monitor systems is presented to enable early detection of critical conditions.
The study presents an algorithmic framework for real-time multi-parameter fusion and anomaly detection in cardiac monitor systems to aid in early detection of critical conditions.
This study presents an algorithmic framework for real-time multi-parameter fusion of biometric data and anomaly detection in cardiac monitor systems. Modern cardiac monitors continuously acquire and analyze multiple physiological parameters, including electrocardiographic signals, heart rate, oxygen saturation, non-invasive or invasive blood pressure, respiratory rate, body temperature, and other vital indicators. The simultaneous analysis of these parameters is essential for early detection of patient deterioration, arrhythmias, hypoxemia, hemodynamic instability, respiratory failure, and other critical conditions.
O.E. et al. (Thu,) conducted a other in Patient deterioration, arrhythmias, hypoxemia, hemodynamic instability, respiratory failure. Algorithmic framework for real-time multi-parameter fusion and anomaly detection was evaluated. An algorithmic framework for real-time multi-parameter fusion of biometric data and anomaly detection in cardiac monitor systems is presented to enable early detection of critical conditions.