Neural Network Ventilator Work of Breathing Prediction
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Solution Overview
Problem
Current ventilator technologies face challenges in accurately and noninvasively predicting a patient's physiologic work of breathing and imposed work of breathing, which are crucial for providing appropriate ventilatory support and determining extubation criteria, due to difficulties in measuring pleural and tracheal pressures.
Innovation Solution
A method and apparatus using a neural network that processes noninvasive parameters from airway pressure, flow, and carbon dioxide sensors to estimate patient effort, including work of breathing, power of breathing, and pressure time product, allowing for more accurate ventilator settings and reduced respiratory muscle fatigue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive measurement methods (esophageal pressure, tracheal pressure) are used to measure work of breathing, then measurement precision is improved, but ease of operation and patient comfort deteriorate due to invasive procedures
Solution Approach 1:
The patent uses an intermediary mathematical model and neural network algorithm that processes easily obtainable ventilator parameters (flow, airway pressure, volume) to indirectly estimate work of breathing. This intermediary computational approach bridges the gap between simple noninvasive measurements and the accuracy previously only achievable through invasive pressure measurements, eliminating the need for esophageal or tracheal catheters while maintaining measurement precision.
Solution Approach 2:
The patent replaces the mechanical invasive measurement system (pressure catheters, esophageal balloons) with a computational system using neural networks and mathematical modeling. The mechanical intrusion into the patient's body is substituted by electronic signal processing and algorithmic estimation, achieving the same measurement goal without physical invasion.
2Ease of operation
If noninvasive parameters are used to estimate work of breathing, then ease of operation is improved, but measurement precision deteriorates due to inability to directly measure pleural and tracheal pressures
Solution Approach 1:
The patent transforms the relationship between measurable parameters and work of breathing by using neural network algorithms that learn complex nonlinear relationships from training data. Instead of relying on simple linear assumptions, the system changes the parameter transformation approach from direct calculation to adaptive computational modeling, significantly improving estimation accuracy from noninvasive inputs.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network is trained using actual invasive measurements as ground truth, then uses the learned model to continuously estimate work of breathing from noninvasive parameters. The feedback loop between training data and operational estimation refines the algorithm's accuracy over time, closing the precision gap between invasive and noninvasive methods.
3Measurement precision
If complex invasive monitoring is implemented to accurately assess patient effort, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent makes the ventilator's existing sensors (flow, pressure, volume) perform multiple functions: they continue to provide standard ventilatory monitoring while simultaneously serving as inputs for work of breathing estimation through the neural network. This multi-functionality eliminates the need for separate invasive monitoring equipment, reducing overall device complexity while maintaining measurement precision.
Solution Approach 2:
The patent extracts the work of breathing estimation function from the complex invasive monitoring system and implements it as a separate computational module using existing ventilator data. By taking out this specific function and implementing it algorithmically, the system avoids the complexity of physical invasive sensors while achieving the same measurement capability.
Data Source
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AI summary
A method of creating a noninvasive predictor of both physiologic and imposed patient effort of breathing from airway pressure and flow sensors attached to the patient using an adaptive mathematical model. The patient effort is commonly measured via work of breathing, power of breathing, or pressure-time product of esophageal pressure and is important for properly adjusting ventilatory support for spontaneously breathing patients. The method of calculating this noninvasive predictor is based on linear or non-linear calculations using multiple parameters derived from the above-mentioned sensors.