Neuromodulation Therapy Simulator with Distributed Computing
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Solution Overview
Problem
Conventional neuromodulation therapy systems face inefficiencies in programming and reprogramming due to limitations in battery life and complexity, making it difficult to optimize therapy parameters for subjects with fluctuating conditions and complex anatomical targets, especially when symptoms vary throughout the day.
Innovation Solution
A distributed computing environment that includes a neuromodulation therapy simulator, allowing for continuous monitoring and modification of therapy parameters based on real-time data from implant devices and external sensors, using machine-learning algorithms to predict optimal physiological responses and update therapy settings on-the-fly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete in-clinic device programming techniques are used, then the implant device can be programmed with therapy parameters, but the treatment optimization is suboptimal and inefficient due to limited clinic visit frequency and symptom variability
Solution Approach 1:
The implant device autonomously performs self-testing and self-programming by automatically evaluating multiple electrode combinations and determining optimal therapy parameters without requiring continuous clinician intervention or frequent clinic visits
Solution Approach 2:
The device performs preliminary automated testing of various electrode configurations and therapy parameters between clinic visits, so that when the clinician does visit, the optimal settings have already been identified and ready for implementation
2Adaptability or versatility
If the implant device performs complex computation to handle increasing therapy complexity, then more complex anatomical targets can be treated, but battery life is limited and computation capacity is constrained
Solution Approach 1:
The computation task is divided into segments: the implant device performs only essential local processing for immediate therapy delivery, while complex computation for therapy optimization is performed externally by the clinician's programming device or server system
Solution Approach 2:
An external computing system (clinician's programming device or server) acts as an intermediary that performs complex computation offline and communicates only essential therapy parameter results to the implant device, reducing the computational burden on the implant's battery
3Measurement precision
If manual adjustment of therapy parameters is performed during clinic visits, then the clinician can observe symptom changes, but symptoms that are difficult to detect during brief visits cannot be properly evaluated
Solution Approach 1:
The implant device automatically performs self-testing to evaluate the subject's response to different electrode combinations and therapy parameters, eliminating the need for the clinician to manually test each configuration during the clinic visit
Data Source
AI summary
Methods and systems are provided for simulating neuromodulation therapy. An identification of a particular neuromodulation-therapy implant device, an identification of a particular neuromodulation-therapy algorithm, and subject records corresponding to a subject may be received. neuromodulation-therapy simulator may execute to generated predicted performance metric for the particular neuromodulation-therapy implant device. The predicted performance may correspond to predicted physiological responses of the subject when the particular neuromodulation-therapy algorithm is executed by the particular neuromodulation-therapy implant device. The predicted performance metrics may be output.