Sleep Stage Prediction Model for Targeted Stimulation Timing
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Solution Overview
Problem
Current sleep monitoring and intervention systems apply continuous sensory stimulation during sleep without considering the subject's current sleep stage or sleep state transitions, leading to potential sleep disturbances and inefficiencies in sleep therapy.
Innovation Solution
A device equipped with sensors and stimulators that use a prediction model to anticipate future sleep stages, adjusting intervention parameters such as timing, intensity, and type of stimulation based on predicted sleep states to minimize sleep disturbances and optimize sleep quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If continuous sensory stimulation is applied during sleep, then sleep therapy coverage is maintained, but sleep disturbances increase and therapy efficiency decreases
Solution Approach 1:
The system performs preliminary classification of sleep stages using a first machine learning model before delivering sensory stimulation. By predicting whether the subject is in or transitioning to REM sleep in advance, the system can proactively adjust or pause stimulation to prevent sleep disturbances while maintaining continuous therapy coverage during appropriate sleep stages.
2Duration of action of stationary object
If continuous sensory stimulation is applied during sleep, then sleep therapy is maintained, but intervention precision deteriorates
Solution Approach 1:
The system employs a two-stage machine learning approach where a first model provides continuous sleep stage classification and a second model refines the prediction by detecting REM sleep transitions. This feedback mechanism continuously adjusts stimulation delivery based on real-time sleep stage detection, ensuring precise intervention timing while maintaining continuous therapy coverage.
3Measurement precision
If sleep stage detection is performed continuously, then sleep stage identification accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the sleep stage detection process into two distinct machine learning models: a first model for continuous sleep stage classification and a second model specifically for detecting REM sleep transitions. This segmentation allows each model to be optimized for its specific function, improving overall detection accuracy while managing computational complexity through specialized processing for different detection tasks.
Data Source
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AI summary
The present disclosure pertains to a system configured to facilitate prediction of a sleep stage and intervention preparation in advance of the sleep stage's occurrence. The system comprises sensors configured to be placed on a subject and to generate output signals conveying information related to brain activity of the subject; and processors configured to: determine a sample representing the output signals with respect to a first time period of a sleep session; provide the sample to a prediction model at a first time of the sleep session to predict a sleep stage of the subject occurring around a second time; determine intervention information based on the prediction of the sleep stage, the intervention information indicating one or more stimulator parameters related to periheral stimulation; and cause one or more stimulators to provide the intervention to the subject around the second time of the sleep session.