Dynamic Slow Wave Detection Criteria Adjustment
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Solution Overview
Problem
Existing sleep monitoring systems detect slow wave sleep using fixed criteria, leading to limited sensory stimulation due to low detection of slow waves, which can hinder the enhancement of slow wave activity and deep sleep quality.
Innovation Solution
A system that adjusts slow wave detection criteria dynamically based on the position of slow waves within a sleep cycle and brain activity signals, enhancing detection sensitivity and timing of sensory stimuli to coincide with detected slow waves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed slow wave detection parameters are used, then the system operation is simple, but the detection precision of slow waves is limited
Solution Approach 1:
The patent implements dynamic adjustment of detection parameters (amplitude threshold, frequency range, duration criteria) based on the subject's individual sleep stage progression and real-time brain activity patterns. The system transitions from fixed parameters to adaptive parameters that change throughout the sleep session, allowing optimal detection across different sleep conditions without requiring multiple fixed parameter sets.
Solution Approach 2:
The system modifies detection parameters including amplitude thresholds, frequency bands, and temporal duration criteria based on detected sleep stages and individual subject characteristics. By changing these parameters dynamically, the system achieves higher detection precision across varying sleep conditions while managing complexity through automated parameter adjustment algorithms.
2Quantity of substance
If fixed detection criteria are used, then the device complexity is low, but the quantity of detected slow waves is insufficient
Solution Approach 1:
The system continuously monitors detected slow wave patterns and uses this feedback to adjust detection parameters in real-time. When the system detects variations in slow wave characteristics or changes in sleep stage, it automatically modifies detection criteria to maintain optimal detection sensitivity, thereby increasing the quantity of accurately detected slow waves without manual intervention.
Solution Approach 2:
The system performs preliminary detection using initial parameters, then uses the results of this preliminary detection to refine and adjust parameters for subsequent detection phases. This staged approach allows the system to build upon initial findings and progressively improve detection quantity and accuracy without requiring the full complexity of adaptive parameters from the start.
3Productivity
If fixed detection parameters are applied throughout the sleep session, then the ease of operation is high, but the productivity of slow wave detection is limited
Solution Approach 1:
The system automatically detects sleep stages, identifies appropriate detection parameters for each stage, and adjusts parameters without user intervention. The automated parameter adjustment and sleep stage detection algorithms enable the system to optimize its own operation, significantly improving detection productivity while maintaining ease of use through a simple user interface that requires no parameter configuration.
Data Source
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AI summary
The present disclosure pertains to a system configured to a system configured to detect slow waves based on adjusted slow wave detection criteria and time delivery of the sensory stimulation to correspond to slow waves detected based on the adjusted criteria. The system is configured to adjust slow wave detection criteria to enhance detection of slow waves in a subject. Slow wave detection using adjustable slow wave detection criteria produces more stimulation relative to prior art systems because more individual stimuli are provided if more slow waves are detected. In some embodiments, the system includes one or more of a sensory stimulator, a sensor, a processor, electronic storage, a user interface, and/or other components.