Electrical Field Models for Neurostimulator Programming
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Solution Overview
Problem
The process of configuring electrical stimulation therapy for implantable neurostimulators is time-consuming and labor-intensive due to the complexity of lead array geometries and the need for trial and error in selecting optimal electrode combinations and parameters, which can lead to inefficiencies and increased clinician burden.
Innovation Solution
A system that generates and displays electrical field models based on patient anatomy and stimulation parameters, allowing clinicians to visualize the areas affected by the therapy and automatically determine effective stimulation parameters, thereby reducing the complexity of programming and optimizing therapy delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial and error method is used to select electrode combinations, then clinician can identify optimal stimulation parameters, but the programming process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary calculations of activation fields and predicts effective electrode combinations before the clinician begins programming. By pre-computing which electrodes will activate target neurons based on lead position and anatomical models, the system eliminates the need for manual trial-and-error testing, significantly reducing programming time while maintaining precision.
Solution Approach 2:
The system creates a virtual copy of the patient's anatomy and lead configuration to simulate and visualize activation fields. This digital twin allows the clinician to preview the effects of different electrode combinations without physically testing them on the patient, reducing programming time while preserving measurement precision through accurate computational modeling.
2Adaptability or versatility
If complex lead array geometries are used to improve stimulation flexibility, then therapy coverage is enhanced, but the complexity of programming increases
Solution Approach 1:
The system introduces an intermediary computational model that translates complex lead array geometries into simplified visual representations of activation fields. Instead of requiring the clinician to manually configure each electrode in complex arrays, the system automatically calculates and displays the combined activation pattern, reducing programming complexity while preserving the versatility of complex lead designs.
Solution Approach 2:
The system adds a visual dimension to the programming interface by displaying three-dimensional activation field models that show which anatomical structures will be stimulated. This visual feedback layer simplifies the programming of complex lead arrays by allowing the clinician to see the overall stimulation pattern rather than manually configuring each electrode parameter, reducing complexity while maintaining flexibility.
3Reliability
If multiple electrode combinations are tested to optimize therapy efficacy, then clinical outcomes improve, but the burden on the clinician increases
Solution Approach 1:
The system provides immediate visual feedback by displaying predicted activation fields for different electrode combinations before the clinician commits to a programming choice. This feedback mechanism allows the clinician to compare multiple options efficiently without the burden of extensive manual testing, as the system pre-calculates and presents the efficacy of various configurations based on the desired target structures.
Solution Approach 2:
The system performs preliminary evaluation of multiple electrode combinations and ranks them based on predicted efficacy for activating target neural structures. By pre-identifying the most promising electrode configurations through computational modeling, the system reduces the clinician's burden while ensuring optimal therapy efficacy is achieved through data-driven selection rather than exhaustive manual testing.
Data Source
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AI summary
The disclosure describes a method and system that generates an electrical field model of defined stimulation therapy arid displays the electrical field model to a user via a user interface. The electrical field model is generated based upon a patient anatomy and stimulation parameters to illustrate which areas of a patient anatomical region will be covered by the electrical field during therapy. In addition, a neuron model may be applied to the electrical field model to generate an activation field model. The activation field model indicates which neurons will be activated by the electrical field in the anatomical region. These field models may be used by a clinician to determine effective therapy prior to stimulation delivery. In particular, the field models may be beneficial when programming non axi-syrnmetric, or three-dimensional (3D), leads which allow greater flexibility in creating stimulation fields.