Parasomnia Episode Prediction with Integrated Pre-Sleep and In-Sleep Models
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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 for ECG, heart rate, and pulse features, combined with deep reinforcement learning for optimal intervention selection, and dynamic deployment of models based on statically and dynamically deployed features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-sleep and in-sleep predictive models are integrated using recurrent neural networks, then reliability of parasomnia episode detection is improved, but device complexity increases
Solution Approach 1:
The patent combines pre-sleep and in-sleep predictive models into a unified recurrent neural network architecture. The RNN integrates temporal sequences from both pre-sleep features (e.g., pre-sleep ECG, heart rate variability) and in-sleep features (e.g., ongoing sleep stage, real-time ECG) to generate comprehensive parasomnia likelihood scores, thereby improving detection reliability through merged multi-stage information.
Solution Approach 2:
The system performs pre-sleep predictive analysis before the actual sleep episode to establish baseline parasomnia risk. This preliminary inference uses pre-sleep physiological features to generate initial likelihood scores that are then fed into the in-sleep model, allowing the system to prepare intervention strategies in advance and improve overall detection reliability.
2Productivity
If deep reinforcement learning is used for optimal intervention selection, then productivity of intervention delivery is improved, but device complexity increases
Solution Approach 1:
The deep reinforcement learning model operates autonomously to select and deliver optimal parasomnia interventions without requiring manual clinical decision-making for each episode. The model learns from historical data and automatically determines the most effective intervention (e.g., audio stimulus, vibration alert) based on real-time parasomnia likelihood scores, thereby improving intervention delivery efficiency.
Solution Approach 2:
The reinforcement learning system implements continuous feedback loops where intervention outcomes are monitored and used to update the policy for future intervention selections. The model receives feedback on whether delivered interventions successfully reduced parasomnia episodes, allowing it to optimize its decision-making and improve productivity over time through learned experience.
3Reliability
If models are dynamically deployed based on training data entry count, then reliability of prediction is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary model training using available training data entries before deployment to ensure minimum reliability thresholds are met. The dynamic deployment mechanism evaluates whether sufficient training data has been collected and only deploys models that meet predetermined reliability criteria, balancing the need for reliable predictions with the time cost of training.
Solution Approach 2:
The model deployment strategy is dynamically adjusted based on the quantity and quality of available training data. The system can transition between different model states (e.g., using pre-trained models when data is scarce, switching to dynamically trained models when sufficient data is available), allowing it to optimize the trade-off between prediction reliability and training time based on current operational conditions.
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.