Cheyne-Stokes Breathing Pattern Classification Using Nasal Flow Signals
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Solution Overview
Problem
Current methods for diagnosing sleep-disordered breathing, particularly Cheyne-Stokes breathing, are inefficient and require expensive, time-consuming polysomnography or polygraphy, which are not readily available, and lack effective screening tools for home use.
Innovation Solution
A digital computer-based pattern classification system using nasal flow data from a small, handheld device like ApneaLink to identify Cheyne-Stokes breathing patterns through filtering, event detection, feature extraction, and classification algorithms, allowing for quick and accurate diagnosis in a home setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography or polygraphy is used for diagnosing sleep-disordered breathing, then diagnostic accuracy is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts only the essential signal processing and pattern recognition functions from complex polysomnography systems, implementing a simplified algorithm that processes basic respiratory signals to detect Cheyne-Stokes breathing patterns. This extraction approach maintains diagnostic accuracy for specific conditions while eliminating unnecessary complexity.
Solution Approach 2:
The patent creates a simplified computational model that copies the essential diagnostic functionality of polysomnography for detecting Cheyne-Stokes breathing. Instead of replicating the entire complex system, it implements a targeted algorithm that reproduces the diagnostic capability for this specific condition using minimal hardware and processing resources.
2Measurement precision
If polysomnography or polygraphy is used for diagnosing sleep-disordered breathing, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The patent implements preliminary automated analysis of respiratory signals using pattern recognition algorithms that can quickly identify Cheyne-Stokes breathing patterns. This preliminary action provides rapid screening results, reducing the time required compared to manual analysis of polysomnography data, while maintaining diagnostic accuracy for the specific condition.
3Measurement precision
If polysomnography or polygraphy is used for diagnosing sleep-disordered breathing, then diagnostic accuracy is improved, but accessibility decreases
Solution Approach 1:
The patent employs a simplified, low-cost computational approach that can be implemented on inexpensive devices or as software algorithms. This approach makes the diagnostic capability accessible in primary care settings and home environments, rather than requiring specialized sleep laboratory infrastructure, thereby significantly improving accessibility while maintaining diagnostic accuracy for Cheyne-Stokes breathing.
Data Source
AI summary
A Cheyne-Stokes (CS) diagnosis system classifies periods of CS-like breathing by examining a signal indicative of a respiratory parameter. For example, nasal flow data is processed to classify it as unambiguously CS breathing or nearly so and to display the classification Processing may detect and display: apnoeas, hypopnoeas, flow-limitation and snore. The signal may be split into equal length epochs and event features are extracted. Statistics are applied to these primary feature(s) to produce secondary feature(s) representing the entire epoch. Each secondary feature is grouped with other feature(s) extracted from the entire epoch rather than from the epoch events. This final group of features is the epoch pattern. The epoch pattern is classified to produce a probability for possible event classes (e.g., Cheyne-Stokes breathing, OSA, etc.). The epoch is assigned to the class with the highest probability, which may both be reported as an indication of disease state.