Closed-loop Therapy Adjustment via Learned Parameter Association
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Solution Overview
Problem
Patients receiving chronic therapy, such as spinal cord stimulation, often need to manually adjust therapy parameters based on changing conditions like activity or posture, which is time-consuming and burdensome.
Innovation Solution
A medical device that senses patient parameters and automatically adjusts therapy by associating detected values with pre-defined therapy information, allowing for closed-loop delivery of stimulation based on learned adjustments, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual therapy adjustment is implemented, then therapy can be customized to patient needs, but patient burden and time consumption increase
Solution Approach 1:
The medical device automatically adjusts therapy parameters by detecting patient symptoms and autonomously selecting appropriate therapy settings from stored programs, eliminating the need for manual patient adjustment while maintaining personalized therapy adaptation to changing patient conditions
2Adaptability or versatility
If manual therapy adjustment is implemented, then therapy can be customized to patient needs, but ease of operation deteriorates
Solution Approach 1:
The device performs self-adjustment by automatically detecting patient symptoms through sensors and selecting appropriate therapy parameters from pre-programmed options, making the system both easy to operate and adaptable to patient needs without requiring manual intervention
3Ease of operation
If automatic therapy adjustment is implemented, then patient burden is reduced, but device complexity increases
Solution Approach 1:
The device stores multiple pre-programmed therapy programs with different parameters before patient use, allowing automatic selection and adjustment based on detected symptoms without requiring complex real-time calculation or processing capabilities
Solution Approach 2:
The system uses sensor feedback to detect patient symptoms and automatically selects appropriate pre-programmed therapy settings, creating a closed-loop control system that reduces patient burden while managing device complexity through structured decision-making algorithms
Data Source
AI summary
Techniques for detecting a value of a sensed patient parameter, and automatically delivering therapy to a patient according to therapy information previously associated with the detected value, are described. In exemplary embodiments, a medical device receives a therapy adjustment from the patient. In response to the adjustment, the medical device associates a sensed value of a patient parameter with therapy information determined based on the adjustment. Whenever the parameter value is subsequently detected, the medical device delivers therapy according to the associated therapy information. In this manner, the medical device may “learn” to automatically adjust therapy in the manner desired by the patient as the sensed parameter of the patient changes. Exemplary patient parameters that may be sensed for performance of the described techniques include posture, activity, heart rate, electromyography (EMG), an electroencephalogram (EEG), an electrocardiogram (ECG), temperature, respiration rate, and pH.