Neurostimulation Lead Placement via Evoked Response Hotspot Analysis
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Solution Overview
Problem
Existing neurostimulation systems face challenges in steering electrostimulation energy to achieve the desired evoked response, particularly in accurately placing neurostimulation leads and programming parameters using evoked responses as feedback.
Innovation Solution
The system employs a method to improve the steering of electrostimulation energy by using feedback from Evoked Response (ER) signals. This involves delivering neurostimulation, sensing ER signals, extracting signal features, computing longitudinal and periodic rotational distributions of these features, and presenting peak regions as a hotspot view on a user interface to guide lead placement and programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evoked responses are used as feedback for neurostimulation lead placement and programming, then the accuracy of lead placement and therapeutic efficacy are improved, but the complexity of the system and difficulty of interpreting evoked response signals increase
Solution Approach 1:
The evoked response signal analysis is segmented into multiple parameters including amplitude, latency, and morphology features. The system divides the complex ER signal into discrete measurable components that can be independently evaluated and combined to assess lead placement accuracy, making the overall complex measurement process more manageable and interpretable
Solution Approach 2:
The system introduces an intermediary processing layer that automatically analyzes evoked response signals and translates them into interpretable metrics and visual displays. This intermediary layer includes algorithms that extract meaningful features from raw ER signals and present them in a clinically useful format, reducing the burden on clinicians to directly interpret complex physiological signals
2Measurement precision
If evoked response signals are used to guide neurostimulation, then the precision of stimulation targeting is improved, but the time required for lead placement and programming increases
Solution Approach 1:
The system performs preliminary analysis of evoked response signals during the programming process itself, rather than requiring separate testing sessions. By continuously monitoring and analyzing ER signals as programming parameters are adjusted, the system provides real-time feedback that guides optimal lead placement and parameter selection, eliminating the need for additional time-consuming separate evaluation steps
Solution Approach 2:
The system dynamically adjusts stimulation parameters based on real-time evoked response analysis. By changing stimulation intensity, pulse width, and frequency parameters while monitoring ER signal responses, the system efficiently identifies optimal settings without requiring exhaustive testing of all possible parameter combinations, thus reducing overall programming time while maintaining precision
3Reliability
If multiple ER signal parameters are analyzed for lead placement guidance, then the reliability of lead placement assessment is improved, but the difficulty of detecting and measuring signals appropriately increases
Solution Approach 1:
The system merges multiple evoked response signal parameters including amplitude, latency, and morphology into a unified assessment framework. By combining these different signal characteristics into a comprehensive lead placement evaluation metric, the system improves reliability through multi-parameter validation while presenting a simplified integrated view that reduces the apparent measurement complexity for the user
Data Source
AI summary
A system may include a stimulus circuit configured to provide electrostimulation to a neural target of a patient via electrodes on a lead, a sensing circuit configured to sense evoked response (ER) signals produced by the electrostimulation, a user interface, and a controller operably connected to the stimulus circuit, the sensing circuit, and the user interface. The controller is configured to initiate delivery of the electrostimulation to the electrodes, identify ER signal features of the sensed ER signals, compute a longitudinal distribution of the ER signal features for a longitudinal direction of the lead, compute a rotational distribution that is a is a periodic distribution of the ER signal features for an angular direction of the lead, and display peak regions of the longitudinal distribution and the rotational distribution as a hotspot view of the sensed ER signals on the user interface.