Parasomnia Episode Prediction With Dynamic Sleep-State Intervention
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Solution Overview
Problem
Existing predictive data analysis solutions for parasomnia episodes are inefficient and unreliable, particularly in real-time scenarios, due to the lack of integration of pre-sleep and in-sleep predictive inferences and the need for environment-specific model deployment.
Innovation Solution
The integration of pre-sleep and in-sleep predictive models using recurrent neural networks and deep reinforcement learning to generate conditional likelihood scores and recommended interventions, along with dynamic deployment of models based on static and dynamic features, ensuring accurate and efficient parasomnia episode prediction and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-sleep and in-sleep predictive models are integrated using recurrent neural networks and deep reinforcement learning, then reliability of parasomnia episode prediction is improved, but device complexity increases
Solution Approach 1:
The patent combines pre-sleep predictive models and in-sleep predictive models into a unified deep reinforcement learning framework. The recurrent neural network integrates temporal sequences from both pre-sleep features (e.g., pre-sleep ECG, pre-sleep behavior) and in-sleep features (e.g., ongoing sleep ECG, sleep stage) to generate comprehensive parasomnia episode predictions, thereby improving reliability through holistic analysis.
Solution Approach 2:
The predictive system is segmented into distinct processing components: pre-sleep feature extraction modules, in-sleep feature extraction modules, recurrent neural network temporal processing units, and deep reinforcement learning intervention selection modules. This segmentation allows each component to specialize in specific tasks while maintaining overall system reliability without excessive complexity.
2Adaptability or versatility
If dynamic deployment of models based on static and dynamic features is implemented, then adaptability to different environments is improved, but productivity decreases
Solution Approach 1:
The system dynamically deploys different model configurations based on static features (e.g., device type, sensor availability) and dynamic features (e.g., current sleep stage, ongoing ECG patterns). The deep reinforcement learning model adapts its prediction horizon and intervention recommendations in real-time based on changing sleep conditions, improving environmental adaptability while maintaining productivity through efficient feature prioritization.
Solution Approach 2:
The model adjusts its operational parameters such as prediction time windows, feature sampling rates, and intervention thresholds based on static deployment context and dynamic sleep state. This allows the system to optimize performance for different environments (e.g., home vs. clinical settings) and real-time conditions without sacrificing overall productivity.
3Measurement precision
If multiple recurrent neural network models process wave, heart rate, and pulse features separately, then measurement precision of parasomnia prediction is improved, but device complexity increases
Solution Approach 1:
The system employs separate recurrent neural network models for processing different physiological feature types: wave feature processing RNN for ECG morphology, heart rate feature processing RNN for temporal heart rate variations, and pulse feature processing RNN for pulse rate and variability. Each specialized model achieves high measurement precision for its specific feature type while the overall complexity is managed through modular architecture and shared underlying RNN structures.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.