Sleep Disorder Detection Using Multi-Signal Pattern Recognition
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Solution Overview
Problem
Current diagnostic systems for sleep disorders, such as polysomnography, are expensive, inconvenient, and not suited for home use or multi-night studies, often missing sleep disordering events and producing false positives due to reliance on predefined thresholds and limited signal modalities.
Innovation Solution
A processor-implemented method that detects sleep disordering events by accessing multiple physiological signals, computing features indicative of patterns, and applying them to a trained classifier to identify sleep disordering events with improved accuracy, using signals like peripheral arterial tone, oxygen saturation, pulse rate, and respiratory effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography is used for sleep disorder diagnosis, then diagnostic accuracy is improved, but cost and convenience deteriorate
Solution Approach 1:
The patent extracts and processes only the most relevant physiological signals (peripheral arterial tone, oxygen saturation, pulse rate, respiratory effort) from the complex polysomnography data, eliminating the need for full polysomnography setup while maintaining diagnostic accuracy through targeted signal analysis and pattern recognition algorithms
Solution Approach 2:
The patent creates a simplified digital copy of the diagnostic process using machine learning classifiers that replicate the diagnostic capability of polysomnography without requiring the physical infrastructure, making the system portable and convenient for home use
2Device complexity
If predefined thresholds are used for event detection, then system simplicity is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent transitions from fixed threshold parameters to dynamic pattern recognition parameters, where the system adapts to individual patient baselines and identifies sleep disordering events based on contextual patterns and deviations from normal behavior rather than static thresholds
Solution Approach 2:
The system continuously monitors physiological signals and uses feedback from pattern recognition to adjust detection sensitivity, allowing the system to learn from each patient's unique sleep patterns and improve accuracy over time without requiring complex manual configuration
3Device complexity
If single signal modality is used for sleep monitoring, then device complexity is reduced, but reliability of detection deteriorates
Solution Approach 1:
The patent merges multiple physiological signal modalities (peripheral arterial tone, oxygen saturation, pulse rate, respiratory effort) into a unified detection framework, where the combination of signals provides complementary information that significantly improves the reliability of sleep disordering event detection compared to any single signal alone
Data Source
AI summary
Apparatus and methods detect sleep disordering events. The apparatus may be configured to access one or more physiological signals generated by one or more sensors. The apparatus may be configured to detect, from the one or more physiological signals, seed events suggestive of sleep disordering events. The apparatus may be configured to compute features indicative of patterns within portions of the one or more physiological signals that are associated with the detected seed events. The apparatus may be configured to apply to a classifier, the computed features indicative of patterns of the seed events. The classifier may be trained to compute a degree of fit of the computed features to learned repetitive patterns of sleep disordering events. The apparatus may be configured to output an identification of sleep disordering event(s) corresponding with the seed events based on the computed degree of fit determined by the classifier.