Waveform Optimization for Neuromodulation Therapy
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Solution Overview
Problem
Current neuromodulation systems are inefficient and time-intensive for patients, as they require frequent clinician visits to modify waveforms, using a trial-and-error approach that limits patient access to optimized therapy.
Innovation Solution
A computing system that retrieves and analyzes historical waveform data to determine preferred parameters, allowing patients to modify and optimize waveforms through a user interface, enabling quick and efficient generation of optimized waveforms for neuromodulation therapy sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a default waveform is used in known neuromodulation systems, then the system is simple to operate, but the waveform cannot be modified to fit individual patient therapeutic needs
Solution Approach 1:
The system enables patients to independently modify waveform parameters through a user interface without requiring clinician intervention. Patients can adjust parameters such as pulse width, frequency, and amplitude to optimize their therapy, and the system automatically processes these changes and transmits updated waveforms to the implantable device.
Solution Approach 2:
The system provides pre-configured waveform templates with optimized parameters that serve as starting points for therapy. These templates are prepared in advance based on clinical guidelines and can be selectively applied to different patient conditions, reducing the need for extensive parameter tuning while still allowing customization when needed.
2Measurement precision
If waveform parameters are modified by trial-and-error with one parameter changed at a time, then the modification process is systematic, but it requires trying a large number of parameter combinations which is time-intensive
Solution Approach 1:
The system transitions from sequential single-parameter adjustment to simultaneous multi-parameter optimization by presenting multiple waveform templates with different parameter combinations. This allows the system to explore the parameter space more efficiently by evaluating multiple dimensions of optimization concurrently rather than incrementally.
Solution Approach 2:
The system incorporates feedback mechanisms where patient responses to different waveform parameters are tracked and used to refine future parameter selections. The system learns from patient feedback about which parameter combinations provide the best therapeutic outcomes and uses this information to recommend optimized waveforms more quickly in subsequent sessions.
3Reliability
If patients visit a clinician each time waveform modification is needed, then the waveform can be professionally adjusted, but the process becomes time-intensive and may dissuade patients from using neuromodulation
Solution Approach 1:
The system empowers patients to independently adjust their own waveform parameters through an intuitive user interface on a mobile device. Patients can modify parameters such as pulse width, frequency, and amplitude directly, and the system automatically transmits these changes to the implantable pulse generator, eliminating the need for repeated clinician visits for routine adjustments.
Solution Approach 2:
The mobile computing device serves as an intermediary between the patient and the implantable pulse generator. It provides a user-friendly interface for parameter adjustment and acts as a communication bridge to transmit optimized waveforms wirelessly to the implanted device, enabling patients to perform waveform optimization without direct clinician involvement.
4Productivity
If multiple waveform parameters are optimized simultaneously, then personalized therapy can be achieved efficiently, but the system complexity increases
Solution Approach 1:
The waveform optimization process is divided into separate modules, each handling specific parameter adjustments. The system segments the complex multi-parameter optimization into manageable components such as pulse width optimization, frequency optimization, and amplitude optimization, allowing each to be processed independently and then combined into a complete waveform solution.
Data Source
AI summary
Provided herein is a computing system for optimizing a waveform, in communication with an implantable pulse generator, and including a computing device including a memory device and a processor communicatively coupled to the memory device. The processor is configured to: retrieve historical waveform data associated with a plurality of waveforms used in therapeutic sessions for a plurality of patients, the historical waveform data including a plurality of waveform parameters; analyzing the historical waveform data to determine preferred waveform parameters; determining that a patient is starting a new therapeutic session using the patient therapeutic device; displaying each of the preferred waveform parameters; prompting the user to accept or modify the displayed waveform parameters; optimizing the waveform parameters for the therapeutic session; and transmitting the optimized waveform parameters to the patient therapeutic device to start the therapeutic session.