Noninvasive Cardiovascular Parameter Estimation via Machine Learning

The patent creates a virtual copy of the arterial system using numerical models that replicate hemodynamic behavior. These models are calibrated to match individual patient anatomy and physiology, allowing accurate CO estimation without physical catheters. The model-based approach copies the essential dynamics of blood flow to derive measurements that would otherwise require invasive instrumentation.

The patent replaces mechanical/invasive measurement systems with computational methods. Instead of using physical catheters, thermodilution equipment, or pulmonary artery monitoring, the system uses numerical simulations calibrated to noninvasive measurements. This substitution eliminates the need for complex invasive hardware while maintaining measurement accuracy.

The patent uses numerical models that create virtual representations of the patient's arterial system, eliminating the need for physical catheterization. These models are calibrated to individual patient characteristics and can accurately predict hemodynamic parameters without introducing foreign objects into the patient's vasculature, thereby avoiding catheterization-related complications.

The patent introduces numerical models as intermediaries between noninvasive measurements and hemodynamic parameter estimation. Rather than directly measuring parameters through invasive means, the models act as computational mediators that translate easily obtained noninvasive data (blood pressure, pulse wave velocity) into accurate hemodynamic estimates without requiring catheterization.

The patent creates a universal measurement system that uses standard, multi-purpose equipment already present in clinical settings. The numerical models can process data from various common sensors (blood pressure cuffs, pulse wave velocity measurements, ECG) to estimate multiple hemodynamic parameters including cardiac output, making the system broadly applicable without requiring specialized single-function devices.

The patent replaces specialized measurement equipment with computational models that can process data from standard clinical devices. By creating virtual representations of hemodynamic systems, the patent enables accurate CO estimation using readily available equipment like blood pressure monitors and pulse sensors, eliminating the need for expensive specialized noninvasive CO measurement devices.

The patent introduces numerical models as computational intermediaries that translate peripheral pressure measurements into accurate central hemodynamic parameters. These models incorporate patient-specific arterial tree characteristics to account for wave reflection and pressure amplification effects, enabling precise central systolic blood pressure estimation from easily obtained peripheral measurements without requiring direct aortic measurement.

The patent applies patient-specific customization to the numerical models, adjusting arterial properties and parameters to match individual anatomy and physiology. This local quality approach ensures that the transformation from peripheral to central pressure measurements accurately reflects each patient's unique vascular characteristics, maintaining high precision while preserving ease of operation.