Respiratory Therapy Interface Identification From Pressure-Flow Signals
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Solution Overview
Problem
Existing respiratory therapy systems face challenges in accurately identifying components such as patient interfaces and estimating therapy parameters, leading to inefficiencies and increased complexity and cost, while also creating compatibility issues and environmental waste.
Innovation Solution
The technology employs automated characterization of respiratory therapy systems through statistical analysis of sensor signals, allowing for robust leak flow rate estimation and adjustment of therapy properties based on identified components, using a controller to analyze pressure and flow rate signals to determine the patient interface and adjust treatment parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated characterization through statistical analysis of sensor signals is implemented, then component identification accuracy and parameter estimation precision are improved, but device complexity and cost are reduced
Solution Approach 1:
The respiratory therapy system performs self-characterization by automatically analyzing sensor signals (pressure and flow rate) to identify patient interface components and estimate therapy parameters. The controller executes statistical analysis algorithms that process measured data to determine system properties without requiring external calibration equipment or manual intervention, thereby improving measurement precision while avoiding additional hardware complexity
Solution Approach 2:
The patent replaces traditional mechanical or manual component identification methods with signal-based statistical analysis. Instead of using physical tags, barcodes, or manual configuration, the system substitutes these with automated analysis of pressure and flow rate sensor signals to identify components and characterize system properties, reducing both hardware complexity and operational burden
2Measurement precision
If automated characterization through statistical analysis of sensor signals is implemented, then parameter estimation precision is improved, but manufacturing cost is reduced
Solution Approach 1:
The system performs self-characterization by automatically analyzing sensor signals to estimate therapy parameters such as leak flow rate, patient interface impedance, and circuit properties. This eliminates the need for expensive factory calibration equipment, test fixtures, and manual calibration procedures, thereby improving parameter estimation accuracy while reducing manufacturing costs
Solution Approach 2:
The patent uses sensor signals (pressure and flow rate measurements) as intermediaries to characterize system parameters. By analyzing these readily available operational signals through statistical methods, the system derives accurate parameter estimates without requiring dedicated calibration hardware or complex manufacturing processes, thus improving measurement precision while keeping manufacturing simple and cost-effective
3Adaptability or versatility
If component identification through signal analysis is implemented, then adaptability to different patient interfaces is improved, but device complexity is reduced
Solution Approach 1:
The controller is designed to universally identify and characterize various patient interface components (masks, nasal pillows, interfaces) through a single automated signal analysis process. The statistical analysis method works across different component types without requiring component-specific hardware or multiple identification systems, thereby improving adaptability while maintaining relatively simple system architecture
Solution Approach 2:
The system adapts to different patient interfaces by detecting changes in system parameters (pressure-flow characteristics, impedance, leak rates) that result from different component configurations. By monitoring how these parameters vary during operation, the system automatically identifies component types and adjusts therapy settings accordingly, improving versatility through software-based parameter detection rather than hardware complexity
Data Source
AI summary
Apparatus and methods provide system characterisation such as for operation of respiratory treatment apparatus. Such a characterisation may include a determination of a patient interface type and/or an event such as a leak or blocked vent. For a characterisation, one or more controller(s) or processor(s) may be configured to make a determination of parameters that best fit a template curve, such as a quadratic function, to a plurality of measurements, such as data points. Each data point may include a pressure value, and a flow rate value at the pressure value. Parameters from the function may then be applied, such as with a data structure to characterize the system, such as with an identification of the patient interface type from the parameters. In some versions, parameter(s) of operation of the apparatus may be adjusted based on the characterisation, such as by using the parameters of the template.