Adaptive Sleep Stage Detection Using Cycle-Specific Algorithms
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Solution Overview
Problem
Existing sleep stage determination methods are inaccurate due to the assumption of stable polysomnography signal patterns throughout a night, which leads to incorrect sleep stage assessments as the patterns change, especially with decreasing slow wave activity.
Innovation Solution
A system and method that utilize sensors and processors to detect individual sleep cycles and apply specific sleep stage detection algorithms and parameters for each cycle, adapting to changes in brain activity patterns, such as those seen in EEG signals, to provide real-time and accurate sleep stage determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static sleep stage determination method is used that assumes stable polysomnography signal patterns throughout a night, then the method complexity is reduced and simpler processing is achieved, but the measurement precision of sleep stage determination deteriorates because the assumption does not reflect actual changing brain activity patterns
Solution Approach 1:
The patent implements dynamic adaptation by detecting individual sleep cycles and switching between different sleep stage determination algorithms based on the current sleep cycle phase. The system transitions from static to dynamic processing by adjusting algorithm parameters according to detected slow wave activity patterns, thereby maintaining accuracy across changing brain activity states while managing complexity through structured adaptation
Solution Approach 2:
The patent changes algorithm parameters based on detected sleep cycle characteristics. Specifically, it modifies the sleep stage determination algorithm parameters according to the detected phase of sleep cycles and slow wave activity levels, allowing the system to adapt to changing polysomnography signal patterns throughout the night without requiring completely different processing approaches
2Ease of operation
If a static sleep stage determination method is used, then the ease of operation is improved through simpler processing, but the reliability of sleep stage assessment deteriorates as signal patterns change during sleep sessions
Solution Approach 1:
The system dynamically adjusts its processing approach by detecting sleep cycles and adapting algorithm parameters in real-time based on detected brain activity patterns. This maintains reliability across changing conditions while preserving ease of operation through automated adaptation rather than requiring manual intervention or complex user configuration
Solution Approach 2:
The system performs self-adjustment by automatically detecting sleep cycles and selecting appropriate algorithm parameters without external intervention. The automated detection and adaptation mechanism maintains reliability while preserving operational simplicity, as the system serves itself by adapting to changing conditions without requiring user input or manual reconfiguration
Data Source
AI summary
The present disclosure pertains to a system and method for determining sleep stages during individual sleep cycles based on algorithms and/or parameters that correspond to the individual sleep cycles. The system enables more accurate real-time sleep stage determinations compared to prior art systems. Sleep cycles are detected in real-time based on an electroencephalogram (EEG), and/or by other methods. At the end of a sleep cycle, the system is configured such that the specific algorithms and/or parameters used for the previous sleep cycle to determine sleep stages are replaced by new ones which are specifically adapted for the next sleep cycle.