Nonlinear Cardiac Output Model via Applanation Tonometry
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Solution Overview
Problem
Current methods for measuring cardiac output are inaccurate and invasive, failing to account for the non-linear processes of left ventricular systolic and diastolic function and central vascular function, leading to suboptimal computation.
Innovation Solution
A non-invasive method using applanation tonometry data and a nonlinear mathematical model that links hemodynamic parameters to cardiac output values through multidimensional optimization, incorporating regression to the mean for accurate calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear approximation strategies are used to compute cardiac output, then the computation is simpler, but the measurement precision deteriorates due to non-linear cardiovascular processes
Solution Approach 1:
The patent transforms the linear approximation approach into a non-linear parameter-based model. By changing from linear to non-linear parameters in the mathematical model, the system accurately captures the non-linear cardiovascular processes while maintaining computational feasibility through structured non-linear equations.
Solution Approach 2:
The patent replaces the simple linear mechanical computation model with a sophisticated non-linear mathematical model that better represents physiological reality. This substitution involves using non-linear differential equations and optimization algorithms to model cardiovascular dynamics, improving measurement precision while managing complexity through computational methods.
2Measurement precision
If invasive methods are used to measure cardiac output, then measurement precision may improve, but the ease of operation and patient comfort deteriorate
Solution Approach 1:
The patent uses applanation tonometry as an intermediary measurement technique. Instead of directly measuring cardiac output through invasive catheterization, the system measures arterial pressure waveforms non-invasively at peripheral sites and uses these as intermediate data to compute cardiac output through non-linear modeling, thereby avoiding direct intrusion into the cardiovascular system.
Solution Approach 2:
The patent replaces invasive mechanical measurement systems with non-invasive optical and pressure sensing systems combined with computational modeling. By substituting direct invasive measurement with non-invasive sensing and mathematical computation, the system achieves comparable or superior precision without compromising patient comfort or ease of operation.
3Ease of operation
If non-invasive methods are used to measure cardiac output, then ease of operation improves, but measurement precision deteriorates due to inability to capture central vascular function accurately
Solution Approach 1:
The patent adds the dimension of temporal dynamics and non-linear relationships to the analysis. By measuring arterial pressure waveforms over time and applying non-linear mathematical models that capture central vascular function dynamics, the system extracts comprehensive physiological information from non-invasive peripheral measurements, thereby improving precision while maintaining ease of operation.
Solution Approach 2:
The patent transforms static or simple dynamic parameters from traditional non-invasive methods into complex non-linear temporal parameters. By analyzing the full waveform characteristics and their non-linear relationships over time, the system captures central vascular function accurately from non-invasive measurements, resolving the precision limitation.
Data Source
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AI summary
Apparatus and methods for calculating cardiac output (CO) of a living subject using applanation tonometry measurements. 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.