Non-Invasive Ventilator Airway Flow Estimation
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Solution Overview
Problem
Non-invasive ventilator systems face challenges in accurately estimating patient airway flow and leak flow due to tubing resistance and compliance, which lead to inaccurate flow measurements at the ventilator, especially without a proximal flow sensor.
Innovation Solution
A non-invasive ventilator system uses a feedback mechanism to minimize the difference between measured and estimated proximal pressure, compensating for leaks by adjusting a known leak flow estimate, and employs a proportional-integral compensator to accurately estimate airway flow and unknown leak flow using remote ventilator pressure and flow sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If remote ventilator sensors are used to monitor flow, then tubing and wires near the patient are reduced, but flow measurement accuracy deteriorates due to tubing resistance and compliance
Solution Approach 1:
The system uses feedback control to minimize the difference between measured proximal pressure and estimated proximal pressure generated by the circuit model. The controller continuously adjusts the model parameters based on the error signal, improving the accuracy of airway flow estimation while using remote sensors.
Solution Approach 2:
A patient circuit model acts as an intermediary between the remote ventilator sensors and the patient airway. The model estimates proximal pressure and airway flow by processing the distal pressure and flow measurements, compensating for the effects of tubing resistance and compliance.
2Reliability
If filtering is applied to pressure waveform measurements, then compression effects are accounted for, but high frequency noise is amplified due to differentiation
Solution Approach 1:
The feedback mechanism minimizes the difference between measured and model-generated proximal pressure waveforms. This closed-loop approach naturally filters noise while maintaining the compression effects, as the model parameters are adjusted to match the actual pressure measurements without requiring direct differentiation.
Solution Approach 2:
The system changes the approach from directly differentiating noisy pressure signals to adjusting model parameters (resistance, compliance) that inherently account for compression effects. This parameter-based approach avoids the noise amplification problem of direct differentiation while still capturing the compression dynamics.
3Measurement precision
If a proximal flow sensor is used to measure airway flow, then flow measurement accuracy is improved, but the risk of occluding the airway with patient secretions increases
Solution Approach 1:
The patient circuit model serves as an intermediary that estimates airway flow without requiring a sensor in the airway. The model processes distal sensor data and circuit parameters to calculate proximal airway flow, eliminating the need for a proximal flow sensor and the associated occlusion risk.
Solution Approach 2:
The system replaces the mechanical proximal flow sensor with a computational model that estimates airway flow. This substitution eliminates the physical presence of a sensor in the airway while providing equivalent or superior measurement accuracy through mathematical modeling and feedback control.
4Object-generated harmful factors
If leak flow is introduced in NIV, then end-tidal CO2 rebreathing is reduced, but airway flow estimation accuracy deteriorates
Solution Approach 1:
The feedback controller minimizes the difference between measured and estimated proximal pressure while accounting for leak flow. The model parameters are continuously adjusted based on the error signal, maintaining accurate airway flow estimation even in the presence of intentional leak flow that prevents CO2 rebreathing.
Solution Approach 2:
The system changes the model parameters to account for leak flow conditions. By adjusting the circuit resistance and compliance parameters in the presence of known leak, the model accurately estimates airway flow despite the altered pressure-flow dynamics caused by the intentional leak.
Data Source
AI summary
A method for estimating patient airway flow in a non-invasive ventilator system. The method includes the steps of (i) determining an estimated gas flow at the proximal end of the tubing; (ii) determining a proximal pressure error value by subtracting the measured pressure at the proximal end of the tubing from the estimated pressure at the proximal end of the tubing; (iii) compensating for the determined proximal pressure estimate error value; (iv) compensating for an error in the estimated gas flow at the proximal end of the tubing by feeding the estimate back into a sum of accumulated flows; (v) determining an estimated gas flow leak; (vi) monitoring for a leak in the non-invasive ventilator system; (vii) determining a gas flow leak factor; (viii) adjusting the estimated gas flow leak; and (ix) compensating for bias in the patient airway flow.