Deep Brain Stimulation Frequency Selection Using HFO and ECA
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Solution Overview
Problem
Current brain stimulators require time-consuming trial and error programming, necessitating a need for automated and objective optimization of programming parameters based on brain response.
Innovation Solution
A method and system using multi-contact electrodes to apply High Frequency Stimulation (HFS) and measure High Frequency Oscillations (HFO) and Evoked Compound Activity (ECA) to identify optimal electrode implantation sites in the brain, adjusting stimulation parameters based on measured electrophysiological markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If trial and error programming is used for brain stimulators, then the programming can be performed with simple manual adjustment, but the programming process becomes extremely time consuming requiring hours per session across multiple sessions
Solution Approach 1:
The system employs closed-loop feedback by continuously monitoring brain responses (HFO and ECA signals) during stimulation and automatically adjusting stimulation parameters based on the measured neural activity. This feedback mechanism replaces manual trial-and-error adjustment with automated real-time optimization, significantly reducing programming time while maintaining simplicity through objective physiological measurements
Solution Approach 2:
The brain stimulation system performs self-adjustment by automatically optimizing stimulation parameters based on real-time monitoring of neural responses. The system uses algorithms that analyze HFO and ECA signals to autonomously determine optimal stimulation settings without requiring continuous manual intervention, enabling the device to self-optimize during therapy
2Loss of time
If automated optimization based on brain response is implemented, then programming time is reduced, but the system complexity increases requiring measurement of HFO and ECA signals
Solution Approach 1:
The electrode system performs multiple functions simultaneously: it delivers high-frequency stimulation, records local field potentials for HFO detection, and measures evoked compound activity. This multi-functionality is achieved through a single integrated platform that combines stimulation and recording capabilities, reducing the need for separate devices and minimizing overall system complexity despite the advanced functionality
Solution Approach 2:
The system uses specific neural signals (HFO and ECA) as intermediaries to bridge the gap between stimulation delivery and parameter optimization. These physiological markers serve as objective mediators that translate complex brain responses into actionable feedback for automated parameter adjustment, simplifying the control algorithm while maintaining scientific rigor
3Measurement precision
If HFO and ECA measurement is performed intraoperatively, then precise implantation site identification is achieved, but the measurement and analysis process becomes more complex
Solution Approach 1:
The system identifies optimal implantation sites by detecting specific parameter thresholds in neural signals. High-frequency oscillations above a predetermined threshold and characteristic evoked compound activity patterns serve as quantitative markers for successful target engagement. These parameter-based criteria transform complex neural activity into simple binary decisions for site acceptance or rejection
Solution Approach 2:
The system performs preliminary testing of multiple electrode contacts before final implantation by applying high-frequency stimulation and measuring HFO/ECA responses at each potential site. This preliminary assessment identifies the optimal contact and depth beforehand, allowing surgeons to confidently proceed with permanent implantation at the pre-validated location without needing to perform extensive post-implantation adjustments
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates rapid and precise identification of ideal implantation sites and calibration of treatment parameters, enhancing the effectiveness of Deep Brain Stimulation (DBS) by adapting to individual brain responses in a closed-loop fashion.
Implementation Method 1
A stimulating device applies High Frequency Stimulation (HFS) to each of the plurality of multi-contact electrodes
Implementation Method 2
The recording device is configured to measure High Frequency Oscillations (HFO) of Local Field Potentials induced in the region of the target structure by the HFS
Implementation Method 3
A range of frequencies for delivering brain stimulation within a predetermined range based on a phase space extracted from the ECA is determined
Implementation Method 4
High Frequency Oscillations (HFO) evoked in the target structure by the applied stimulation frequencies within the determined range are measured
Data Source
AI summary
A method of determining brain stimulation parameters includes applying Low Frequency Stimulation (LFS) to a contact of a multi-contact electrode implanted in a target structure in an individual's brain. Evoked Compound Activity (ECA) evoked in the target structure by the LFS is measured. A range of frequencies for delivering brain stimulation within a predetermined range based on a phase space extracted from the ECA is determined. Stimulation frequencies are applied to the contact of the multi-contact electrode within the determined range of frequencies. High Frequency Oscillations (HFO) evoked in the target structure by the applied stimulation frequencies within the determined range are measured. A frequency evoking HFO above a predetermined threshold is determined. The determined frequency is selected as a treatment frequency for the target structure.