Sleep Stimulation Timing via Prediction Model
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Solution Overview
Problem
Existing sleep monitoring and stimulation systems either lack accuracy or are intrusive, failing to provide reliable sensory stimulation during deep sleep while maintaining user comfort.
Innovation Solution
A system that uses historical sleep depth information to train a prediction model, determining when a user is in deep enough sleep for stimulation, and delivers sensory stimulation accordingly, combining EEG data with non-invasive monitoring devices to enhance sleep quality without arousals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG sensor based systems are used to monitor sleep, then measurement precision of sleep stages is improved, but device complexity and user comfort deteriorate due to sensors and wiring on the scalp
Solution Approach 1:
The patent uses an intermediary prediction model that takes input from simplified non-EEG sensors (wearable devices) and outputs predicted sleep stages. This mediator translates easily collected sensor data into accurate sleep stage predictions without requiring direct EEG contact with the scalp, thus maintaining precision while reducing complexity
Solution Approach 2:
The system creates a virtual copy of EEG-based sleep stage detection through the prediction model. Instead of directly measuring brain waves with EEG sensors, the system uses a trained model to replicate and predict sleep stage patterns from alternative sensor inputs, achieving similar measurement goals with simpler hardware
2Ease of operation
If non-EEG sensor based systems are used to monitor sleep, then ease of operation and user comfort are improved, but measurement precision of sleep stages deteriorates
Solution Approach 1:
The prediction model continuously receives feedback from wearable sensors and adjusts its sleep stage predictions accordingly. This feedback loop allows the system to maintain high measurement precision by constantly refining its predictions based on actual sensor data patterns, while the user experiences no discomfort from scalp sensors
Solution Approach 2:
The patent replaces the mechanical/physical EEG sensor system with a computational prediction model. Instead of using physical contact sensors on the scalp to detect brain waves, the system substitutes a software-based prediction mechanism that processes data from comfortable wearable devices to achieve accurate sleep stage identification
3Reliability
If sensory stimulation is delivered during deep sleep, then sleep restoration and cognitive benefits are improved, but reliability of stimulation delivery deteriorates due to inability to accurately detect deep sleep stages
Solution Approach 1:
The prediction model performs preliminary analysis of sleep patterns and predicts upcoming deep sleep stages before they occur. This advance prediction allows the system to prepare and time sensory stimulation delivery to coincide with predicted deep sleep periods, ensuring reliable delivery without requiring real-time precise detection
Solution Approach 2:
The system uses the user's own historical sleep data and patterns to self-determine optimal stimulation timing. The prediction model leverages the user's unique sleep architecture to autonomously identify deep sleep windows, eliminating the need for external monitoring devices or complex real-time detection systems while maintaining high reliability
Data Source
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AI summary
The present disclosure pertains to a system and method for providing sensory stimulation (e.g., tones and/or other sensory stimulation) during sleep. The delivery of the sensory stimulation is timed based on a combination of output from a trained time dependent sleep stage model and output from minimally obtrusive sleep monitoring devices (e.g. actigraphy devices, radar devices, video actigraphy devices, an under mattress sensor, etc.). The present disclosure describes determining whether a user is in deep sleep based on this information and delivering sensory stimulation responsive to the user being in deep sleep. In some embodiments, the system comprises one or more sensory stimulators, one or more hardware processors, and/or other components.