Nonlinear Cardiac Output Computation via Applanation Tonometry
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Solution Overview
Problem
Current methods for measuring cardiac output are invasive, inaccurate, and suboptimal due to the use of linear approximation strategies that fail to capture the non-linear nature of left ventricular systolic and diastolic function and central vascular processes.
Innovation Solution
A non-invasive method using applanation tonometry to measure hemodynamic parameters, which are then processed through a nonlinear mathematical model to calculate cardiac output, incorporating multidimensional optimization and regression to minimize systematic bias and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear approximation strategies are used to compute cardiac output, then the measurement process is simple, but the accuracy is poor due to the non-linear nature of cardiac function
Solution Approach 1:
The patent transforms the cardiac output computation from a linear approximation to a non-linear mathematical model that accounts for the non-linear hemodynamic relationships. The model uses power laws and exponential functions to represent the non-linear interactions between cardiac function and vascular properties, thereby improving measurement precision while maintaining computational feasibility through parameter transformation.
Solution Approach 2:
The patent replaces complex mechanical measurement systems (invasive catheterization, echocardiography) with a computational approach that uses non-invasive tonometric data. The mechanical complexity of direct cardiac measurement is substituted with mathematical modeling that processes simpler peripheral measurements to derive cardiac output, achieving both non-invasiveness and improved accuracy.
2Measurement precision
If invasive methods are used to measure cardiac output, then the measurement can be direct, but the procedure becomes complex and carries risks
Solution Approach 1:
The patent introduces peripheral tonometric measurements as an intermediary to access cardiac output information. Instead of directly measuring cardiac function through invasive means, the system uses non-invasive tonometry at peripheral sites to capture hemodynamic signals, which are then processed through mathematical models to derive cardiac output. This intermediary approach maintains measurement accuracy while eliminating invasive procedures.
Solution Approach 2:
The patent creates a computational copy of the cardiac output measurement process. Rather than physically invasive measurement, the system generates a mathematical representation that replicates the measurement function using non-invasive data. The tonometric signal serves as a proxy that, when processed through the non-linear model, produces cardiac output values equivalent to direct measurement but without the invasive risks.
3Ease of operation
If non-invasive methods are used, then the procedure is simpler, but the accuracy is insufficient compared to direct measurement
Solution Approach 1:
The patent adds the dimension of non-linear mathematical transformation to the measurement process. By incorporating power laws, exponential functions, and interactive terms that capture non-linear hemodynamic relationships, the system transforms simple tonometric data into accurate cardiac output measurements. This dimensional addition of mathematical complexity compensates for the lack of direct physical measurement.
Solution Approach 2:
The patent implements feedback through iterative model refinement and validation. The non-linear mathematical model is calibrated using population data and validated against reference measurements, allowing continuous improvement of accuracy. The model incorporates feedback loops that adjust parameters based on observed hemodynamic patterns, ensuring that the non-invasive measurements converge toward accurate cardiac output values.
Data Source
AI summary
Apparatus and methods for calculating cardiac output (CO) of a living subject. In one embodiment, the apparatus and methods build a nonlinear mathematical model to correlate physiologic source data vectors to target CO values. The source data vectors include one or more measurable or derivable parameters such as: systolic and diastolic pressure, pulse pressure, beat-to-beat interval, mean arterial pressure, maximal slope of the pressure rise during systole, the area under systolic part of the pulse pressure wave, gender (male or female), age, height and weight. The target CO values are acquired using various methods, across a plurality of individuals. Multidimensional nonlinear optimization is then used to find a mathematical model which transforms the source data to the target CO data. The model is then applied to an individual by acquiring physiologic data for the individual and applying the model to the collected data.