Customized Sleep Management System for Disorder Detection
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Solution Overview
Problem
Existing technologies fail to accurately detect various sleep disorders and provide effective stimulation during specific sleep steps, leading to inadequate sleep quality improvement, as they rely on limited bio-signals and lack real-time analysis and machine learning-based customization.
Innovation Solution
A customized sleep management system that collects and processes electrophysiological and non-electrophysiological signals to classify sleep steps and detect disorders, providing tailored stimulation using a computing device and stimulation modules for improved sleep quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only specific bio-signals such as electroencephalogram signals or electromyogram signals are used to detect sleep disorders, then the detection process is simple, but the accuracy of sleep disorder detection is insufficient
Solution Approach 1:
The patent combines multiple bio-signals (electroencephalogram, electromyogram, electrooculogram) and non-bio signals (motion, sound, temperature) into a unified sleep analysis system. This merging of multiple signal sources enables comprehensive sleep disorder detection by analyzing various physiological parameters simultaneously, thereby improving detection accuracy without requiring separate independent systems for each signal type.
Solution Approach 2:
The sleep analysis system is designed to perform multiple functions: it detects various sleep disorders (insomnia, sleep apnea, restless legs syndrome, etc.), classifies sleep stages, and provides customized stimulation recommendations. This multi-functional approach allows a single system to address diverse sleep issues using the same set of collected signals, improving measurement precision across different disorder types.
2Adaptability or versatility
If existing technologies detect specific sleep disorders to provide notification service or suggest stimulation, then the system is easy to operate, but the system cannot simultaneously detect various sleep disorders that degrade sleep quality
Solution Approach 1:
The patent segments the sleep analysis process into distinct modules: signal collection from multiple sources, pre-processing of raw signals, feature extraction, sleep stage classification, and specific sleep disorder detection. This segmentation allows the system to handle multiple disorder types systematically by analyzing different signal features for each disorder, thereby improving versatility while managing complexity through modular architecture.
Solution Approach 2:
The system transitions from analyzing single-dimensional signals to multi-dimensional signal analysis by incorporating both electrophysiological signals (brain waves, muscle activity) and non-electrophysiological signals (motion, sound, temperature). This dimensional expansion enables the detection of various sleep disorders simultaneously, as each disorder manifests through different combinations of signal dimensions, thereby improving adaptability.
3Adaptability or versatility
If uniform stimulation is provided for sleep disorder alleviation, then the implementation is simple, but the stimulation is not optimized for individual sleep disorder characteristics and sleep steps
Solution Approach 1:
The stimulation system dynamically adjusts stimulation parameters based on real-time sleep stage classification and detected sleep disorder characteristics. Instead of providing fixed uniform stimulation, the system modifies stimulation intensity, type, and timing according to the user's current sleep state (wakefulness, NREM stages, REM) and specific disorder being treated, thereby improving adaptability while using feedback mechanisms to manage control complexity.
Solution Approach 2:
The patent implements customized stimulation by varying multiple stimulation parameters including intensity, frequency, duration, and type of stimulation based on individual sleep disorder characteristics and sleep stages. This parameter customization allows the system to optimize treatment effectiveness for each user's specific condition, transforming uniform stimulation into adaptive, personalized intervention while maintaining manageable complexity through systematic parameter adjustment rules.
Data Source
AI summary
The present invention provides a customized sleep management method performed by a customized sleep management system including a step of collecting a bio-signal by using a computing device; a step of generating user sleep data by performing a pre-processing process on the collected bio-signal; a step of classifying sleep steps by using a sleep analysis model receiving the user sleep data and detecting sleep disorder appearing in the classified sleep steps; and a step of providing customized stimulation for alleviating the detected sleep disorder. The bio-signal includes at least one of an electrophysiological signal and a non-electrophysiological signal.