Variable Scheduling for Low Power Medical Devices
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Solution Overview
Problem
Medical device systems face significant energy drains due to computational processing demands, particularly in ambulatory and implantable devices, which can lead to reduced battery life and the need for costly and risky power source replacements.
Innovation Solution
Implementing a system that dynamically manages signal acquisition and data analysis by scheduling based on the estimated susceptibility of neurological events, using feature extractors and classifiers to adjust the frequency and intensity of measurements and analysis, thereby conserving energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous signal acquisition and data analysis are performed to monitor neurological conditions, then monitoring effectiveness is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the scheduling of signal acquisition and data analysis based on the estimated susceptibility of neurological events. When susceptibility is high, the system increases monitoring frequency; when susceptibility is low, it reduces frequency. This dynamic adaptation allows the system to maintain monitoring effectiveness while significantly reducing energy consumption compared to continuous fixed-rate monitoring.
Solution Approach 2:
The system changes the parameter of monitoring frequency based on the output of feature extractors and classifiers. By adjusting the scheduling parameters (time between measurements, duration of analysis) according to the estimated susceptibility, the system optimizes the balance between monitoring reliability and energy consumption.
2Measurement precision
If more frequent signal acquisition and analysis are scheduled to detect neurological events, then detection accuracy is improved, but battery life decreases
Solution Approach 1:
The system implements dynamic scheduling where the frequency of signal acquisition and analysis is adjusted based on real-time estimates of neurological event susceptibility. This allows the system to perform more frequent, accurate detection when needed while reducing frequency during low-risk periods, thereby extending battery life without compromising detection accuracy when it matters most.
Solution Approach 2:
The system uses feedback from feature extractors and classifiers that analyze physiological signals to determine susceptibility levels. This feedback loop enables the system to automatically adjust the scheduling of measurements and analysis, optimizing the balance between detection accuracy and energy consumption to extend battery life.
3Measurement precision
If computationally intensive analysis methods are used to improve condition assessment, then assessment accuracy is improved, but power consumption increases
Solution Approach 1:
The system dynamically selects and schedules data analysis methods based on the estimated susceptibility of neurological events. When susceptibility is high, the system employs more computationally intensive analysis methods to improve assessment accuracy. When susceptibility is low, it uses less intensive methods or reduces analysis frequency, thereby reducing power consumption while maintaining accuracy when needed.
Solution Approach 2:
The system changes the computational intensity parameter of analysis methods based on susceptibility estimates. By adjusting which analysis methods are applied and how frequently they are run according to the output of feature extractors and classifiers, the system optimizes the trade-off between assessment accuracy and power consumption.
Data Source
AI summary
Medical device systems and methods for operating medical device systems conserve energy by efficiently managing computational demands of the systems. Signals from a subject are processed and analyzed and an estimate of a propensity for a subject to have a neurological event is determined. Based on the results of the analysis and the estimate, further analysis may be performed and the estimate may be refined. Succeeding cycles of signal measurement and analysis are scheduled depending on the results of the analysis and the estimate. The schedule may be varied temporally or with regard to the types and intensities of analyzes performed.