Sleep Quality Evaluation Using Neural Network Analysis
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Solution Overview
Problem
Current methods for quantifying sleep quality are hindered by high costs, complexity, inconvenience, and inaccuracy, particularly due to limited access to necessary hardware and expert interpretation, making long-term sleep quality monitoring inaccessible and expensive.
Innovation Solution
A computer-implemented system that processes sleep data from sensors using neural networks to determine quality metrics such as AHI, sleep stages, and sleep events, enabling consistent and cost-effective monitoring of sleep health over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert interpretation of sleep data is used, then measurement precision is improved, but cost increases and accessibility decreases
Solution Approach 1:
The patent applies the copying principle by replacing expensive expert interpretation with an AI-based system that replicates expert analysis capabilities. The neural network model is trained on comprehensive sleep data datasets, creating a virtual copy of expert interpretation that can process sleep studies independently, thereby reducing costs and increasing accessibility while maintaining measurement precision.
Solution Approach 2:
The patent substitutes the mechanical system of human expert review with an automated AI-based processing system. The neural network algorithm replaces the manual analysis performed by sleep specialists, enabling automated interpretation of sleep data that maintains accuracy while eliminating the cost and accessibility barriers associated with human experts.
2Reliability
If long-term sleep monitoring is implemented, then reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by designing a multi-functional AI system that can handle various aspects of sleep monitoring through a single integrated platform. The neural network model performs multiple functions including sleep stage classification, apnea detection, and quality assessment, thereby improving reliability for long-term monitoring without proportionally increasing device complexity.
Solution Approach 2:
The patent implements self-service by creating an autonomous AI system that independently processes and interprets sleep data without requiring continuous human intervention. The neural network automatically analyzes sleep studies, generates reports, and identifies patterns over time, enabling reliable long-term monitoring while keeping system complexity manageable through automation.
3Measurement precision
If comprehensive sleep data collection is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming the approach to handling comprehensive sleep data. Instead of increasing hardware complexity to collect more data, the system uses neural network algorithms that can process existing multi-parameter sleep data (heart rate, oxygen saturation, movement) to derive comprehensive quality metrics, thereby maintaining measurement precision while controlling device complexity.
Data Source
AI summary
The present technology relates to systems and methods for evaluating a user's sleep data. More particularly, the present technology contemplates a computer-implemented system or method of evaluating a quality of sleep for a user based on sleep data, such as by determining a set of quality metrics for the user's sleep based on the sleep data using a set of probabilistic models.