Closed-Loop Pain Management System with Multi-Sensor Adaptive Neurostimulation
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Solution Overview
Problem
Current pain management systems rely on subjective patient reports for pain assessment, which can be inefficient and impractical, especially in ambulatory settings, and require manual programming by clinicians, limiting timely adjustments to pain therapy.
Innovation Solution
A closed-loop pain management system utilizing multiple sensors to generate a multi-sensor indicated pain score through physiological and functional signal metrics, with an electrostimulator and controller to deliver adaptive neurostimulation therapy based on the pain score, allowing for automatic adjustment of stimulation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective patient reports are used for pain assessment, then pain management can be provided, but the system becomes inefficient and impractical especially in ambulatory settings
Solution Approach 1:
The system enables automatic pain assessment and therapy adjustment through physiological signal monitoring and automated control algorithms, eliminating the need for continuous manual patient reporting and clinician intervention. The closed-loop system self-regulates stimulation parameters based on real-time pain state detection.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where physiological signals (such as heart rate variability, skin conductance, or other pain-related biomarkers) are continuously monitored, processed to determine pain state, and used to automatically adjust neurostimulation therapy parameters, creating a real-time adaptive control system.
2Reliability
If manual programming by clinicians is used, then pain therapy can be adjusted, but timely adjustments are limited when patients lack immediate professional medical assistance
Solution Approach 1:
The system empowers patients to receive appropriate pain management in ambulatory settings by automatically assessing pain state and adjusting therapy without requiring clinician presence. The device performs self-diagnosis and self-adjustment based on physiological signals.
Solution Approach 2:
The system introduces an automated control algorithm as an intermediary between the patient's physiological state and the neurostimulation therapy delivery, replacing the need for direct clinician-patient interaction while maintaining therapeutic accuracy.
3Measurement precision
If multiple sensors and automated control are implemented, then timely and accurate pain assessment is enabled, but device complexity increases
Solution Approach 1:
The system uses existing physiological signal sensors (heart rate, skin conductance, respiration) that can detect multiple pain-related parameters simultaneously, allowing one sensor system to serve multiple assessment functions rather than requiring dedicated sensors for each parameter.
Solution Approach 2:
The system combines multiple signal processing functions and control algorithms into a single integrated pain state determination module, consolidating the complexity of analyzing multiple physiological signals and adjusting multiple stimulation parameters into a unified control system.
Data Source
AI summary
Systems and methods for managing pain in patient are described. A system may include sensors configured to sense physiological or functional signals, and a pain analyzer to generate signal metrics from the physiological or functional signals. The pain analyzer also generates weight factors corresponding to the signal metrics. The weight factors may indicate the signal metrics reliability in representing an intensity of the pain. The pain analyzer generates a pain score using a plurality of signal metrics and a plurality of weight factors. The pain score may be output to a user or a process. The system may additionally include an electrostimulator to generate and deliver closed-loop pain therapy according to the pain score.