Self-adaptive SCS therapy generation via physiological monitoring
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Solution Overview
Problem
Current spinal cord stimulation (SCS) devices are inefficient and inconvenient, relying on manual, time-consuming processes for setting and adjusting electrical parameters, lacking intelligence and self-adaptation to patient physiological changes, and requiring frequent physician interventions for optimal pain relief.
Innovation Solution
A self-adaptive system that integrates sensors to monitor physiological parameters, uses neural networks and genetic algorithms to automatically detect discomfort, classify pain levels, and generate optimal stimulation programs, allowing the device to adapt and learn without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual trial-and-error methods are used to set electrical parameters, then the system is simple to operate, but it is time-consuming and inefficient
Solution Approach 1:
The system automatically monitors physiological parameters and generates stimulation programs without requiring manual intervention. The processor self-adjusts electrical parameters based on real-time sensor data, eliminating the need for trial-and-error manual settings and significantly improving parameter setting efficiency.
Solution Approach 2:
The system continuously monitors physiological parameters through sensors and uses this feedback to automatically adjust stimulation parameters. This closed-loop feedback mechanism enables the system to adapt to patient conditions in real-time, improving efficiency while maintaining appropriate system complexity through automated control.
2Adaptability or versatility
If the system lacks self-adaptation capabilities, then the device complexity is lower, but it cannot adapt to patient physiological changes
Solution Approach 1:
The system dynamically adjusts stimulation parameters based on real-time physiological monitoring. The processor automatically modifies electrical parameters in response to changing patient conditions, enabling the system to adapt to patient physiological changes while managing complexity through programmed adaptation algorithms.
Solution Approach 2:
The system performs self-adjustment based on sensor feedback without requiring external intervention. The processor automatically generates and modifies stimulation programs according to monitored physiological parameters, providing adaptability to patient changes while containing system complexity within the automated control framework.
3Loss of time
If frequent physician interventions are required, then the system remains simple, but it increases time loss and medical costs
Solution Approach 1:
The system automatically monitors physiological parameters and generates appropriate stimulation programs without requiring frequent physician interventions. This self-service capability reduces the time needed for physician visits while managing complexity through automated monitoring and control functions.
Solution Approach 2:
The system uses continuous physiological monitoring feedback to automatically adjust stimulation parameters, eliminating the need for frequent manual adjustments by physicians. This feedback mechanism reduces time loss for medical visits while containing system complexity within the automated control loop.
4Extent of automation
If the system does not automatically detect discomfort, then the device is simpler, but it cannot provide intelligent pain management
Solution Approach 1:
The system replaces manual mechanical monitoring with automated electronic sensing and processing. Sensors automatically detect physiological parameters related to discomfort, and the processor interprets this data to trigger appropriate stimulation programs, providing intelligent pain management while managing complexity through electronic automation.
Solution Approach 2:
The system automatically monitors and detects patient discomfort through physiological sensors without requiring manual assessment. The processor self-evaluates sensor data and automatically initiates appropriate stimulation programs, providing intelligent pain management while containing complexity within the automated detection and response system.
Data Source
AI summary
A system for treating pain consisting of an “add-on” module integrated within the implantable pulse generator (IPG) component of IPG spinal cord stimulators, or, alternatively, integrated within the radiofrequency (RF) transmitter component of RF spinal cord stimulators. The system automatically and continuously monitors, measures, and classifies multiple patient physiological parameters without human intervention. The system also classifies qualitative perceptive changes felt by the patient. On the basis of this input vector information, the system automatically and continuously generates the most appropriate stimulation programs to improve, alleviate, or eliminate the patient's pain without human intervention. The system automatically and continuously adapts itself to both quantitative physiological changes within the patient and qualitative perceptive changes felt by the patient.