Breath Event Sleep Staging Using Respiratory Signal Analysis
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Solution Overview
Problem
Current CPAP systems provide limited feedback on the effect of therapy on objective measured sleep quality, lacking effective metrics to assess sleep stage determination and breathing disorders.
Innovation Solution
A sleep staging system that utilizes sensors to generate output signals conveying breathing parameters, and physical computer processors to determine breathing features, distribute these features over moving time windows, and classify sleep states using a sleep stage classifier model, providing feedback on sleep states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CPAP systems provide feedback focused on therapy parameters (AHI, usage history, leaks), then therapy monitoring is improved, but sleep quality assessment capability deteriorates
Solution Approach 1:
The CPAP system is enhanced to perform multiple functions: it continues to monitor therapy parameters (AHI, usage, leaks) while simultaneously assessing sleep quality through breath event detection and sleep stage classification. The system uses the same breath signal processing infrastructure to serve both therapy monitoring and sleep assessment purposes, eliminating the need for separate monitoring systems.
Solution Approach 2:
The system introduces breath event detection as an intermediary mechanism that bridges therapy monitoring and sleep quality assessment. By detecting breath events (apneas, hypopneas, hyperpneas) and classifying sleep stages from breath signals, the system creates a common ground that connects therapy effectiveness with sleep outcomes, providing unified feedback that addresses both therapeutic and sleep quality dimensions.
2Measurement precision
If sleep stage classification uses traditional methods (EEG, EOG, EMG), then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system extracts sleep stage classification capability from the complex multi-sensor EEG/EOG/EMG system and implements it using solely breath signal processing. By taking out the sleep stage determination function and re implementing it based on breath events and respiratory patterns, the system achieves the same measurement precision with dramatically reduced device complexity and cost.
Solution Approach 2:
The system replaces the mechanical/electrical sensor array (EEG electrodes, EOG sensors, EMG sensors) with a simpler breath signal measurement system. Instead of using multiple specialized sensors to detect sleep stages, the system uses breath flow or respiratory effort signals processed through algorithms that classify sleep stages based on respiratory characteristics, substituting a simpler measurement approach for a more complex one.
3Measurement precision
If breath event detection analyzes individual breath characteristics, then sleep classification performance is improved, but processing time and computational complexity increase
Solution Approach 1:
The system segments the sleep night into discrete epochs (e.g., 30-second intervals) and processes breath events within each epoch separately. By dividing the continuous sleep signal into manageable time segments, the system can apply detailed breath event analysis to each segment without overwhelming computational resources, and the results can be aggregated to provide overall sleep stage classification. This segmentation enables high-precision analysis while controlling processing time.
Data Source
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AI summary
The present disclosure describes a sleep staging system. The sleep staging system comprising: one or more sensors configured to generate output signals conveying information related to one or more breathing parameters of subject; and one or more physical computer processors operatively connected with the one or more sensors, the one or more physical computer processors configured by computer readable instructions to: determine, based on the output signals, one or more breathing features of individual breaths of the subject; determine a distribution of the one or more breathing features over one or more time windows; detect presence of a breathing event based on the output signals; determine sleep states of the subject with a sleep stage classifier model based upon the distribution of the breathing features and the one or more breathing events; and provide feedback indicating the sleep states.