Neuromodulation Therapy Platform 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 computational capacity of implant devices, making it challenging to optimize therapy parameters for complex conditions like Parkinson's disease and chronic pain, which require continuous monitoring and adaptation.
Innovation Solution
A distributed computing environment that includes a neuromodulation therapy platform with mobile devices and cloud-based servers, allowing for continuous monitoring and automatic adjustment of therapy parameters based on real-time data from implant devices and external sensors, using machine-learning algorithms to optimize therapy settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete in-clinic programming sessions are used to adjust therapy parameters, then the implant device can be programmed for specific subjects, but the treatment solutions become suboptimal and less efficient due to inability to continuously monitor and adapt to fluctuating symptoms
Solution Approach 1:
The system transitions from discrete in-clinic programming sessions to continuous monitoring and adjustment of therapy parameters. The implant device continuously senses brain signals and automatically adjusts stimulation parameters in real-time, ensuring continuous optimization of therapy without requiring repeated clinical visits.
Solution Approach 2:
The implant device is equipped with automated algorithms that enable it to self-adjust therapy parameters based on real-time sensing of brain signals and subject response. This self-service capability eliminates the need for manual reprogramming by clinicians and allows the device to adapt to fluctuating symptoms autonomously.
2Measurement precision
If more computation is performed on the implant device to handle complex therapies, then therapy optimization improves, but battery life is limited and computational capacity is constrained
Solution Approach 1:
The computational workload is segmented between the implant device and external systems. The implant device performs essential real-time processing for immediate therapy adjustment, while more complex computation and data analysis are performed externally, reducing the computational burden and energy consumption of the implant device.
Solution Approach 2:
The system dynamically adjusts computational parameters based on available energy resources. When battery charge is sufficient, more intensive computation is performed for optimal therapy optimization. When energy is limited, the system reduces computational intensity to conserve battery life, adapting the level of processing to current energy availability.
Data Source
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AI summary
Methods and systems are provided for generating specific software implementations of neuromodulation-therapy algorithms. A dataset may be received that includes operational specifications that correspond to a plurality of types of neuromodulation-therapy implant devices. A neuromodulation-therapy design interface may provide a representation of the neuromodulation-therapy implant device. A selection if a particular representation of a particular neuromodulation-therapy implant device may be received. Hardware characteristics of the particular neuromodulation-therapy implant device may be used to determine constraints of the implant device. A listing of neuromodulation-therapy parameters that is constraining according to the constraints may be presented. In response to a selection of a parameter, executable software code corresponding to a specific implementation of a neuromodulation-therapy algorithm may be generated. The executable software code may be transmitted to a computing device compatible with the specific implementation.