Non-Invasive Arterial Parameter Detection via Neural Network and Windkessel Model
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Solution Overview
Problem
Current methods for determining arterial compliance, conduction resistance, and blood flowability are hindered by measurement uncertainties, limited reproducibility, and the need for invasive procedures, with pulse contour-based methods often failing to clearly separate these parameters due to superimposed waves and tissue interference.
Innovation Solution
A method using a trained neural network to analyze pulse curves, combined with an electro-hydraulic artery model, allows for non-invasive determination of arterial compliance, line resistance, and blood inertia by simulating the arterial tree and applying correction factors to improve measurement accuracy and reproducibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If pulse contour analysis is used to determine arterial parameters, then non-invasive measurement is achieved, but measurement precision deteriorates due to superimposed waves and tissue interference
Solution Approach 1:
The arterial system is segmented into multiple sections (proximal, middle, distal) with different Windkessel models applied to each segment. This allows separation of wave reflections from different anatomical locations, enabling precise determination of local arterial parameters despite superimposed waves in the composite pulse waveform.
Solution Approach 2:
A multi-section Windkessel model serves as an intermediary framework that mathematically separates the contributions of different arterial segments to the observed pulse waveform. This mediator model enables extraction of precise local parameters (compliance, resistance, inertia) from the composite signal contaminated by tissue interference and wave superposition.
2Ease of operation
If characteristic points of pulse waveform are used to determine surrogate parameters, then non-invasive measurement is possible, but measurement precision deteriorates due to difficulty in clearly determining position and amplitude
Solution Approach 1:
The mechanical/visual method of identifying characteristic points on pulse waveforms is replaced with a mathematical model-based approach using multi-section Windkessel models. This substitution transforms the problem from subjective visual estimation to objective parameter extraction through model fitting, significantly improving precision of position and amplitude determination.
3Productivity
If arterial parameters are measured using traditional methods, then measurement can be performed, but reliability deteriorates due to measurement uncertainties and limited reproducibility
Solution Approach 1:
The measurement system incorporates feedback through iterative optimization where the multi-section Windkessel model parameters are adjusted to minimize the difference between simulated and measured pulse waveforms. This feedback mechanism ensures reliable and reproducible determination of arterial parameters by continuously refining the model fit to the actual physiological data.
Data Source
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AI summary
In order to detect arterial parameters, an averaged pulse curve of a human being is fed to a calculating unit. Said calculating unit adapts the parameters of a simulation model, a so-called analogue, in such a manner that the averaged pulse curve corresponds to the pulse curve of the simulation model. The arterial parameters output by the analogue can be displayed in a radar chart.