Respiratory Insufficiency Detection Using Neural Network Inference
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Solution Overview
Problem
Conventional systems for detecting respiratory insufficiency in non-intubated subjects, based on capnogram analysis, fail to accurately distinguish between small, unproductive breaths and normal tidal breathing, and are misled by cardiogenic oscillations, leading to inadequate detection of insufficient respiration.
Innovation Solution
A system utilizing an artificial neural network that infers a key parameter of breathing, such as expired volume, from determined breathing parameters, and compares it to a threshold to detect respiratory insufficiency, incorporating sensors for CO2, oxygen, flow, pressure, and humidity to differentiate between valid and artifact breaths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional capnometry is used to monitor CO2 levels, then the system can detect breathing patterns, but it cannot distinguish between small unproductive breaths and normal tidal breathing
Solution Approach 1:
The patent combines multiple sensing modalities (capnometry for CO2 detection, accelerometer for motion detection, and temperature sensor for thermal changes) into an integrated monitoring system. This fusion of sensors allows the system to cross-validate signals and distinguish between true respiratory events and artifacts, thereby improving breath detection accuracy while preserving breath quality information
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes the relationship between CO2 levels, motion patterns, and temperature changes. This intermediary analysis helps differentiate between unproductive breaths (which would show CO2 changes without corresponding motion patterns) and normal tidal breathing (which shows coordinated changes across all parameters), thus resolving the information loss problem
2Reliability
If conventional systems detect small gas movements, then they can identify potential breathing, but they misinterpret cardiogenic oscillations as sufficient respiration
Solution Approach 1:
The patent applies dynamic analysis to distinguish between different types of gas movements by examining their temporal and spatial characteristics. Cardiogenic oscillations exhibit specific patterns (high frequency, low amplitude, regular intervals corresponding to heart rate) that differ from true respiratory patterns. The system dynamically adapts its interpretation based on the detected pattern, improving both reliability and measurement precision
Solution Approach 2:
The patent utilizes the periodic nature of cardiogenic oscillations (synchronized with heart rate) versus the typically irregular or slower periodicity of respiratory movements. By analyzing the periodicity and rhythm of detected gas movements, the system can reliably distinguish between heart-induced oscillations and genuine breathing events, even when both produce similar small amplitude signals
3Productivity
If the system monitors CO2 fluctuation during inhalation and exhalation, then it can track breathing, but it fails to detect insufficient respiration in sedated or obstructed subjects
Solution Approach 1:
The patent establishes baseline respiratory patterns and sets threshold criteria for adequate breathing before clinical events occur. By comparing real-time measurements against these pre-established standards, the system can proactively detect when breathing becomes insufficient, rather than merely tracking CO2 fluctuations. This preliminary framework enables early detection of respiratory compromise in sedated or obstructed subjects
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously compares detected breathing parameters against expected ranges and adjusts its assessment accordingly. When CO2 fluctuations indicate potential insufficiency, the feedback loop triggers additional analysis using other sensor data to confirm whether true respiratory failure is occurring, thereby improving detection reliability without reducing monitoring effectiveness
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of respiratory insufficiency detection by distinguishing between valid and artifact breaths, improving the assessment of respiratory sufficiency in subjects with conditions like over-sedation or airway obstruction.
Implementation Method 1
one or more sensors (e.g., a capnometric sensor) that generate output signals that convey information related to one or more parameters of gas at or near the airway
Data Source
AI summary
Respiratory insufficiency is detected by classifying preliminary breaths identified through a capnogram as being valid or artifact. Individual breaths are classified as being valid or artifact by determining values of a plurality of breathing parameters for a given breath, inferring a value for a key parameter from the determined values for the plurality of breathing parameters, and comparing the inferred value for the key parameter to a predetermined threshold.