Closed-Loop Stimulation for Slow Wave Activity Optimization
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Solution Overview
Problem
Current technologies fail to effectively optimize slow wave activity (SWA) during sleep, which is crucial for physical and mental health, as insufficient or altered SWA patterns are associated with various diseases and conditions.
Innovation Solution
An apparatus and method that utilize dominant peripheral nervous system (PNS) oscillations to determine optimal windows for stimulation, combining bio-signals like heart rate, blood pressure, and EEG signals to enhance SWA generation through closed-loop stimulation systems, adjusting based on user feedback to maximize EEG SWA and other rhythms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stimulation is delivered during sleep to enhance SWA, then SWA generation is improved, but timing precision is insufficient without considering PNS oscillations
Solution Approach 1:
The system continuously monitors PNS bio-signals (heart rate, blood pressure, breathing) and uses this feedback to dynamically determine optimal stimulation timing windows. The feedback loop adjusts stimulation delivery based on real-time detection of dominant PNS oscillations, ensuring precise timing that maximizes SWA enhancement while accounting for individual physiological variability.
Solution Approach 2:
The system identifies and targets specific parameters of PNS oscillations (frequency, amplitude, phase) to determine optimal stimulation windows. By analyzing changes in these parameters across different sleep stages, the system dynamically adjusts stimulation timing to coincide with phases most conducive to SWA generation, thereby improving both reliability and timing precision.
2Reliability
If multiple bio-signals are monitored to determine optimal stimulation windows, then SWA optimization is improved, but system complexity increases
Solution Approach 1:
The system uses a single integrated platform that performs multiple functions: monitoring multiple PNS bio-signals (heart rate, blood pressure, breathing), classifying sleep stages, detecting dominant oscillations, determining optimal stimulation windows, and controlling stimulation delivery. This multi-functional approach improves SWA optimization accuracy while avoiding the complexity of separate dedicated devices for each function.
Solution Approach 2:
The system combines monitoring of multiple different bio-signals (cardiovascular, respiratory, neural) into a unified analysis framework that identifies dominant PNS oscillations. By merging these diverse signal sources and processing them through a common algorithmic approach, the system achieves high optimization accuracy without proportionally increasing system complexity.
3Reliability
If stimulation timing is adjusted based on dominant PNS oscillations, then SWA enhancement is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system uses readily measurable PNS bio-signals (heart rate, blood pressure, breathing) as intermediaries to indirectly detect and characterize central nervous system oscillations that directly influence SWA. These peripheral signals serve as accessible proxies that reflect the timing and phase of brain oscillations, making detection feasible while maintaining high SWA enhancement effectiveness.
Solution Approach 2:
The system replaces direct measurement of cerebral oscillations (which would require invasive EEG monitoring) with non-invasive measurement of peripheral physiological signals. By substituting mechanical/electrical measurement of brain activity with measurement of cardiovascular and respiratory parameters, the system reduces detection difficulty while preserving the ability to timing stimulation for SWA enhancement.
Data Source
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AI summary
Embodiments in accordance with the present disclosure are directed to methods and apparatuses used for slow wave activity (SWA) optimization. An example method includes receiving one or more bio-signals from a user and classifying sleep stages by processing the bio-signals. The method further include determining dominant peripheral nervous system (PNS) oscillations based on the bio-signals and as a function of time and stage of sleep, and characterizing at least one property of the dominant PNS oscillations, including a phase, a phase shift, an amplitude, and/or frequency. The method further include providing an indication of an optimal window for maximizing SWA generation based on the phase, the phase shift, the amplitude, or the frequency. The indication is provided to stimulation circuitry that delivers stimulation to the user within the optimal window. Feedback is provided responsive to the stimulation based on an EEG signal of the user.