Adaptive Therapy Parameter Updates for Low-Power Neurostimulation
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Solution Overview
Problem
Existing implantable neurostimulation systems face challenges in efficiently managing power and computational resource consumption due to constant sensing and stimulation update rates, which can lead to increased energy burden and reduced battery longevity.
Innovation Solution
A system that dynamically adjusts the sensing and stimulation update rates based on detected patient activity levels and battery status, using a co-processor to optimize therapy control by increasing or decreasing update rates as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the implanted device continuously monitors physiological parameters and automatically updates therapy control parameters, then the quality of life for patients with neurological disorders is improved through adaptive therapy, but the device complexity and power consumption increase
Solution Approach 1:
The system segments the control parameter update process into discrete events triggered by physiological parameter changes. Rather than continuous monitoring and updating, the system divides the operation into distinct phases: monitoring phase, detection phase (when a change threshold is met), and update phase (when new parameters are applied). This segmentation reduces the computational burden and device complexity while maintaining adaptive therapy benefits.
Solution Approach 2:
The system changes its operational parameters dynamically by adjusting the update frequency based on physiological activity. When physiological parameters remain stable, the system reduces monitoring intensity and update frequency. When significant changes are detected, the system increases activity to update therapy parameters. This parameter adaptation reduces overall power consumption and complexity while preserving therapeutic effectiveness.
2Reliability
If the device updates therapy control parameters frequently to adapt to changing physiological conditions, then the therapeutic effectiveness is improved, but the battery life is reduced
Solution Approach 1:
The system implements periodic monitoring of physiological parameters at predetermined time intervals rather than continuous monitoring. Between monitoring events, the device enters a low-power state. This periodic action ensures that therapy parameters are updated when needed while significantly extending battery life by reducing the active operation time of the device.
Solution Approach 2:
The system uses feedback from physiological parameter measurements to intelligently control when updates occur. The monitoring circuit detects changes in physiological parameters and triggers parameter updates only when predetermined change thresholds are exceeded. This feedback mechanism ensures therapeutic effectiveness is maintained by updating parameters when actually needed, while avoiding unnecessary updates that would drain the battery.
3Adaptability or versatility
If the system monitors physiological parameters continuously and updates parameters in real-time, then the adaptability of therapy is improved, but the power consumption increases
Solution Approach 1:
The system dynamically adjusts its monitoring and update behavior based on physiological conditions. When physiological parameters are stable, the system reduces monitoring frequency and enters power-saving modes. When significant changes are detected, the system becomes more active to capture and respond to the changes. This dynamic operation maintains high adaptability when needed while reducing power consumption during stable periods.
Solution Approach 2:
The system applies partial monitoring action by not continuously monitoring all parameters at full resolution. Instead, it monitors at reduced intensity during stable periods and increases monitoring only when changes are detected. This partial action approach maintains the ability to detect significant physiological changes while significantly reducing overall power consumption compared to full continuous monitoring.
Data Source
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AI summary
An example of a system for delivering a therapy may include a therapy output device to deliver the therapy and a therapy control circuit to control the delivery of the therapy using sensed therapy-control signals. The therapy control circuit may include a therapy controller to control the delivery of the therapy using therapy parameters, a therapy parameter adjuster to adjust the therapy parameters using one or more sensed input parameters, a physical state detector to detect a physical state of the patient using one or more physical signals of the sensed therapy-control signals, a measurement system to measure one or more signals of the sensed therapy-control signals at a sensing update rate and to produce the one or more sensed input parameters based on the measurement, and an update rate adjuster to adjust the sensing update rate based on one or more rate-adjusting parameters including the detected physical state.