Apnea Classification System Using Multi-Sensor Segmentation
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Solution Overview
Problem
Current methods for detecting sleep apnea struggle to differentiate between obstructive and central sleep apnea, which is crucial for determining the appropriate therapy.
Innovation Solution
An apnea classification system that utilizes multiple sensors, including respiration-based and non-respiration-based detectors, to monitor and differentiate between the two types of sleep apnea by analyzing various parameters, such as thoracic impedance, blood pressure, and heart sound signals, to determine the best treatment approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If respiration-based detection methods are used, then apnea events can be detected, but the type of sleep apnea cannot be differentiated
Solution Approach 1:
The patent segments the detection system into multiple specialized detectors: a respiration-based apnea detector for detecting breathing cessation and a non-respiration-based apnea detector for detecting specific apnea types. Each detector processes specific parameters independently, and their results are combined in an apnea classification module to achieve accurate apnea type differentiation without requiring a single overly complex detector.
Solution Approach 2:
The patent adds a new dimension of detection by incorporating non-respiration-based parameters (such as blood pressure, heart sound, or other physiological signals) alongside traditional respiration-based parameters. This multi-dimensional approach enables the system to differentiate between obstructive and central sleep apnea by analyzing patterns across multiple physiological domains simultaneously.
2Measurement precision
If multiple sensors are used to differentiate apnea types, then diagnostic accuracy improves, but device complexity increases
Solution Approach 1:
The patent designs the apnea classification system with multi-functional capability: the same sensor system serves both to detect general apnea events (respiration-based function) and to differentiate apnea types (non-respiration-based function). The apnea classification module universally processes inputs from multiple sensor types to perform both detection and classification functions, reducing the need for separate specialized systems.
Solution Approach 2:
The patent merges multiple detection functions into a unified apnea classification system. The respiration-based apnea detector and non-respiration-based apnea detector are combined in the apnea classification module, which integrates their outputs to simultaneously achieve apnea event detection and apnea type differentiation, thereby managing complexity through functional integration rather than separate systems.
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
Enables accurate differentiation between obstructive and central sleep apnea, allowing for tailored therapy adjustments, thereby improving treatment effectiveness.
Implementation Method 1
the respiration-based apnea detector comprises of at least one of a respiration sensor, an impedance sensor
Implementation Method 2
A second apnea detector is non-respiration-based and is indicative of a specific type of apnea. The non-respiration-based apnea detector may be implemented in several ways. In one example, the non-respiration-based apnea detector comprises of at least one of a pressure sensor
Implementation Method 3
the respiration-based apnea detector comprises of at least one of a respiration sensor, an impedance sensor, a pressure sensor, an accelerometer
Data Source
AI summary
An apnea classification system provides for apnea monitoring and differentiation based on several sleep apnea related parameters for diagnostic and therapeutic purposes. Monitoring of such sleep apnea related parameters allows the apnea classification system to differentiate among the different types of apnea. This information may then be used to determine the best method of therapy, or adjust current therapy parameters to more effectively treat a subject.