Neural Network Sleep Stimulation Timing
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Solution Overview
Problem
Existing sleep monitoring and sensory stimulation systems fail to accurately deliver sensory stimulation during sleep due to their state-based approaches, which do not account for individual user characteristics such as age and demographic parameters, leading to inadequate stimulation timing and intensity.
Innovation Solution
A system utilizing sensors, hardware processors, and neural networks to generate output signals conveying brain activity information, train a neural network with historical sleep depth data, and predict deep sleep stages to modulate the timing and intensity of sensory stimulation based on intermediate layer values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If state-based sleep monitoring systems are used to deliver sensory stimulation, then the system structure is simple and easy to implement, but the stimulation timing and intensity do not account for individual user characteristics leading to inadequate personalization
Solution Approach 1:
The system performs preliminary actions by training a neural network model offline using historical sleep data from multiple users. This pre-trained model captures individual user characteristics and sleep patterns, enabling personalized stimulation delivery during actual sleep sessions without requiring complex real-time adjustments. The preliminary training phase separates the complexity from the real-time operation, resolving the contradiction between personalization and system complexity.
Solution Approach 2:
The system creates a computational copy of user sleep patterns through neural network models. Instead of directly measuring and responding to real-time brain activity with complex algorithms, the system uses trained neural networks that replicate the relationship between EEG patterns and sleep stages. This copying approach enables personalized stimulation delivery while maintaining relatively simple real-time system architecture.
2Measurement precision
If EEG-based sleep stage detection is used to trigger sensory stimulation, then the response time is fast, but the detection accuracy does not account for demographic parameters such as age
Solution Approach 1:
The system performs preliminary training of neural networks using historical EEG data from users with known demographic characteristics and verified sleep stages. This offline preparation allows the model to learn age-specific and individual sleep patterns without adding complexity to the real-time detection system. During actual sleep monitoring, the pre-trained model provides accurate, personalized sleep stage detection by processing EEG signals through the learned parameters.
3Reliability
If sensory stimulation is delivered based on simple sleep stage thresholds, then the system is easy to operate, but the stimulation timing may not adequately correspond to individual sleeping patterns
Solution Approach 1:
The system implements self-service by automatically adapting to each user's individual sleeping patterns without requiring manual configuration or complex user input. The neural network model is trained on the user's historical sleep data and automatically learns their unique sleep architecture, circadian rhythms, and response characteristics. During operation, the system autonomously adjusts stimulation timing and parameters based on the trained model's predictions, maintaining simplicity while achieving high reliability.
Data Source
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AI summary
The present disclosure pertains to a system and method for delivering sensory stimulation to a user during a sleep session. The system comprises one or more sensors, one or more sensory stimulators, and one or more hardware processors. The processor(s) are configured to: determine one or more brain activity parameters indicative of sleep depth in the user based on output signals from the sensors; cause a neural network to indicate sleep stages predicted to occur at future times for the user during the sleep session; cause the sensory stimulator(s) to provide the sensory stimulation to the user based on the predicted sleep stages over time during the sleep session, and cause the sensory stimulator(s) to modulate a timing and/or intensity of the sensory stimulation based on the one or more brain activity parameters and values output from one or more intermediate layers of the neural network.