Sleep Study System Sensor Intervention Timing
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Solution Overview
Problem
Existing overnight sleep study systems often disrupt patients' sleep by requiring interventions during sub-optimal sleep stages, as they lack the ability to determine the most appropriate time for maintenance or repair based on the current sleep stage, leading to potential disturbances and suboptimal measurement quality.
Innovation Solution
A sleep study system that uses sensors to monitor physiological parameters and sleep stages, with a processor determining the optimal time for intervention to minimize sleep disturbance, by analyzing sensor outputs and predicting when interventions are necessary, allowing for real-time alerts and minimizing disruptions during less disruptive sleep stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intervention is performed immediately when sensor malfunction is detected, then measurement quality is maintained, but sleep disturbance increases
Solution Approach 1:
The system performs preliminary action by detecting sensor malfunction and predicting the optimal intervention time before actually performing the intervention. The processor continuously monitors sleep stages and determines when the patient is most likely to be in a deep sleep stage, then schedules the intervention for that predicted optimal time, thereby avoiding sleep disturbance while ensuring measurement quality is maintained.
Solution Approach 2:
The system applies dynamics by adapting the intervention timing based on the patient's real-time sleep stage. Instead of a fixed immediate intervention, the system dynamically adjusts when the intervention occurs based on continuous monitoring of sleep stages, allowing the technician to intervene during the most appropriate moment (deep sleep stage) to minimize disturbance while maintaining measurement quality.
2Object-affected harmful factors
If intervention is delayed until deep sleep stage, then sleep disturbance is minimized, but response time to sensor malfunction increases
Solution Approach 1:
The system performs preliminary action by continuously monitoring sleep stages and pre-determining the optimal intervention time before the actual malfunction occurs. The processor tracks sleep stage transitions and identifies when the patient enters deep sleep stage, then schedules the intervention for that predicted optimal moment, thereby minimizing sleep disturbance while maintaining acceptable response time through advance planning.
Solution Approach 2:
The system applies feedback by continuously monitoring sleep stages and using this information to determine the optimal intervention time. The processor receives feedback from sleep stage monitoring, compares it against the detected sensor malfunction, and adjusts the intervention timing accordingly, creating a closed-loop system that balances response time with sleep disturbance minimization.
3Productivity
If technician enters patient's room immediately, then sensor issue is resolved quickly, but patient sleep is further disturbed
Solution Approach 1:
The system performs preliminary action by detecting sensor malfunction and predicting the optimal intervention time before the technician actually enters the room. The processor monitors sleep stages in advance and determines when the patient is most likely to be in a deep sleep stage, then schedules the technician's intervention for that predicted optimal time, thereby resolving the contradiction between quick resolution and sleep disturbance.
Solution Approach 2:
The system applies self-service by using automated monitoring and prediction to determine intervention timing, reducing the need for the technician to immediately respond to sensor malfunctions. The system serves itself by continuously analyzing sleep stages and providing guidance on optimal intervention timing, allowing the technician to act at the most appropriate moment rather than immediately upon detection.
Data Source
AI summary
A sleep study system comprises a set of sensors for monitoring physiological parameters of a subject during sleep as part of a sleep study and for monitoring the sleep stage of the subject. It is determined if intervention to the subject is needed for maintenance or repair to the sleep study system. If so, a time to perform the intervention is also derived based on the sleep stage of the subject, in particular so as to be least disruptive to the subject.