Respiratory Severity Clustering Beyond AHI for Sleep Assessment
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Solution Overview
Problem
The existing AHI scale is unreliable for diagnosing and treating sleep disorders, particularly for individuals with low or medium AHI scores, and there is a need for additional metrics to improve diagnosis and treatment.
Innovation Solution
A method for determining respiratory severity metrics by analyzing respiratory air flow data, identifying events, clustering them, and calculating a clustering index, which includes positional data to assess sleep disorders more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AHI scale is used to assess sleep disorders, then a standardized measurement is provided, but the measurement precision and reliability deteriorate for individuals with low or medium AHI scores (5-25)
Solution Approach 1:
The patent segments the assessment of sleep disorders by dividing respiratory events into clusters based on temporal proximity and severity characteristics. Instead of treating all events uniformly as AHI does, the system groups events into clusters and calculates cluster-specific metrics, allowing differentiated assessment that captures the nuanced severity patterns that AHI misses, particularly for borderline cases.
Solution Approach 2:
The patent adds multiple dimensions to the assessment beyond AHI's single metric. It introduces temporal dimension (clustering patterns), severity dimension (amplitude of flow reduction), and positional dimension (sleep position during events). This multi-dimensional approach transforms the one-dimensional AHI into a comprehensive severity index that resolves the precision-reliability contradiction.
2Ease of operation
If only AHI is used for diagnosis, then the diagnostic process is simple, but the diagnostic accuracy deteriorates due to lack of additional metrics
Solution Approach 1:
The system performs self-service by automatically collecting raw respiratory data, identifying events, clustering them, calculating severity metrics, and generating comprehensive diagnostic reports without requiring manual intervention. This automation maintains simplicity while dramatically improving accuracy through multi-parameter analysis, resolving the contradiction between ease of operation and diagnostic precision.
Solution Approach 2:
The patent transforms the diagnostic approach by changing from a single parameter (AHI count) to multiple parameters including cluster count, cluster duration, severity amplitude, and positional data. This parameter expansion improves diagnostic accuracy while the automated calculation methods keep the process simple and manageable.
3Measurement precision
If detailed respiratory event analysis is performed, then measurement precision improves, but device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-defining event detection thresholds, cluster formation criteria, and severity calculation algorithms before data analysis. This pre-programming allows complex multi-parameter analysis to be executed automatically without real-time complexity, achieving high measurement precision while keeping the operational system relatively simple.
Data Source
AI summary
Methods and systems for assessment, treatment, and/or prevention of positional sleep therapy for sleep apnea and other disorders are disclosed. In one example, a method for determining at least one respiratory severity metric for an individual's sleep session is provided. The method may include steps of receiving a data signal indicative of respiratory air flow of the individual as a function of time; identifying a plurality of respiratory severity events from the data signal; calculating respiratory severity values for each of the plurality of identified respiratory events; grouping the plurality of respiratory events into a plurality of clusters; and calculating, for each cluster, an accumulated respiratory severity value as a clustering index.