Closed-Loop Neurostimulation for Sleep Quality
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Solution Overview
Problem
Current neurostimulation devices for improving sleep quality lack individualization and adaptability, relying on non-targeted stimulation patterns without real-time feedback control, which limits their effectiveness across diverse populations and requires expert supervision.
Innovation Solution
A system that monitors neurophysiological signals in real-time, updates a physiological and behavioral model, and generates a neurostimulation intervention schedule to administer timed interventions in phase with detected slow-wave neural activity, refining the schedule based on continuous feedback from sensors and user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If neurostimulation devices use non-targeted stimulation patterns without real-time feedback control, then device complexity is reduced, but sleep quality improvement effectiveness deteriorates
Solution Approach 1:
The patent implements real-time feedback control by monitoring neurophysiological signals (EEG, EOG, EMG, HRV) during sleep and using this information to dynamically adjust neurostimulation parameters. The system continuously adapts stimulation timing, duration, and intensity based on detected sleep stages and neural activity patterns, transforming open-loop devices into closed-loop systems that respond to individual sleep physiology in real-time
Solution Approach 2:
The system transitions from static, pre-programmed stimulation patterns to dynamic, adaptive neurostimulation that changes in real-time based on sleep stage detection. The device automatically adjusts stimulation parameters according to detected slow-wave sleep periods, REM phases, and other neural states, allowing the same device to deliver different stimulation protocols throughout the night without manual intervention
2Ease of operation
If neurostimulation devices operate without individualized control, then ease of operation is improved, but adaptability to diverse populations deteriorates
Solution Approach 1:
The system performs automated sleep stage classification and neurostimulation parameter optimization without requiring user input or expert intervention. The device self-adjusts based on its own sensor data, automatically identifying sleep stages and determining optimal stimulation timing, thereby maintaining ease of operation while achieving high adaptability through autonomous decision-making
Solution Approach 2:
The patent employs multiple parameter changes including stimulation intensity, frequency, duration, and timing based on detected sleep physiology. The system modifies these parameters dynamically throughout the night according to sleep stage transitions, allowing a single device to adapt to diverse sleep patterns and individual differences across populations without requiring manual reconfiguration
3Device complexity
If neurostimulation interventions are not timed to neural activity, then device complexity is reduced, but sleep quality improvement deteriorates
Solution Approach 1:
The system implements periodic neurostimulation interventions that are precisely timed to coincide with detected slow-wave sleep periods and neural oscillation patterns. Rather than continuous or random stimulation, the device applies periodic pulses synchronized with the user's natural sleep rhythm, maximizing the effectiveness of each stimulation episode while maintaining manageable device complexity through rhythm-based control
4Manufacturing precision
If expert supervision is required for neurostimulation control, then manufacturing precision and effectiveness are improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The automated feedback system continuously monitors sleep physiology and adjusts stimulation parameters in real-time, replacing the need for expert supervision. The system's closed-loop control automatically optimizes timing, intensity, and duration based on detected neural patterns, achieving manufacturing-level precision in stimulation delivery while maintaining consumer-level ease of operation through autonomous decision-making
Solution Approach 2:
The device performs self-optimization of stimulation parameters without requiring expert intervention or manual configuration. The automated algorithm continuously learns from sleep data and adjusts parameters to maximize effectiveness, allowing consumers to use the device independently while receiving precision control that previously required expert supervision
Data Source
AI summary
Described is a system for adaptable neurostimulation intervention. The system monitors a set of neurophysiological signals in real-time and updates a physiological and behavioral model. The set of neurophysiological signals are classified in real-time based on the physiological and behavioral model. A neurostimulation intervention schedule is generated based on the classified set of neurophysiological signals. The system activates electrodes via a neurostimulation intervention system to cause a timed neurostimulation intervention to be administered based on the neurostimulation intervention schedule. The neurostimulation intervention schedule and timed neurostimulation intervention are refined based on new sets of neurophysiological signals.