Respiratory Therapy Device Using Dynamic CO2 Estimation
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Solution Overview
Problem
Conventional respiratory therapy devices for treating sleep-disordered breathing, such as CPAP and bi-level machines, lack the ability to estimate CO2 levels without additional sensors, which can be uncomfortable and costly, and do not provide timely flushing of CO2 to prevent breathing events like apneas and hypopneas.
Innovation Solution
A method and device that estimate blood or lung CO2 levels using a dynamic simulation model driven by observed respiration signals, such as flow rate or pressure, to predict breathing events and adjust therapeutic pressure cyclically to enhance CO2 flushing, employing a ramp cycle therapy to mitigate triggers for breathing events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional CO2 sensors are added to respiratory therapy devices, then CO2 level measurement capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a simulation model as an intermediary that processes readily available respiration signals (flow rate, pressure) to estimate CO2 levels. This mediator translates existing sensor data into meaningful CO2 level information without requiring direct CO2 sensors, thus avoiding the complexity and cost of adding new hardware while still achieving the measurement capability.
Solution Approach 2:
The patent creates a virtual copy of the respiratory system through a simulation model that replicates CO2 level behavior. Instead of directly measuring CO2 with physical sensors, the system creates a computational replica that mirrors the physiological CO2 levels based on observed respiration patterns, providing indirect measurement without additional hardware.
2Device complexity
If conventional respiratory therapy devices are used, then device simplicity is maintained, but ability to detect and respond to CO2 buildup is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the simulation model continuously estimates CO2 levels and this information feeds back to trigger therapeutic pressure adjustments. When CO2 buildup is detected through the model, the system automatically responds by increasing ventilatory support, creating a closed-loop control system that enhances reliability without complex hardware modifications.
Solution Approach 2:
The simulation model performs preliminary detection of CO2 buildup trends before critical levels are reached. By analyzing respiration signals in advance, the system can predict impending breathing events and initiate preventive therapeutic actions, improving reliability through early intervention rather than reactive response.
3Productivity
If high frequency ventilation modes are used, then CO2 flushing capability is improved, but patient comfort and spontaneous breathing compatibility deteriorate
Solution Approach 1:
The patent employs dynamic pressure adjustments that adapt to the patient's spontaneous breathing pattern. Rather than imposing a fixed high-frequency rhythm, the system dynamically modulates therapeutic pressure in response to detected respiratory signals, allowing CO2 flushing to occur naturally with the patient's own breathing while still providing effective ventilatory support when needed.
Solution Approach 2:
The system applies periodic ventilatory support that is synchronized with the patient's natural breathing cycle. By delivering therapeutic pressure in periodic bursts that align with spontaneous breaths, the system enhances CO2 elimination through rhythmic ventilation while maintaining patient comfort and avoiding the disruption of continuous high-frequency modes.
Data Source
AI summary
A method of controlling a medical device is disclosed for delivering respiratory therapy to a user to treat sleep-disordered breathing, for instance obstructive sleep apnea, Cheyne-Stokes respiration etc. by estimating the user's CO2 percentage or concentration from a dynamic lung model driven by an observed respiration signal. The estimated user's CO2 percentage or concentration can be used to predict breathing events, such as hypopnea and apnea. The predictive capacity can be used for adjusting the respiratory therapy as required or for applying a ramp cycle therapy, in an attempt to reduce the prevalence and adverse effects of the breathing events. In other examples a variable ventilation therapy is provided in which pressure is supplied between first and second pressures, with the pressure being increased over more than one breath, and then dropped relatively rapidly, for example during expiration of a single breath.