Deep Brain Stimulation Frequency Selection with ECA and HFO
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current brain stimulators require time-consuming trial and error for programming, necessitating multiple sessions and lack automated, 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 in a closed-loop fashion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If trial and error programming method is used, then programming can be performed with simple equipment, but programming time is extremely long and multiple sessions are needed
Solution Approach 1:
The system measures HFO and ECA signals from the brain in real-time during stimulation and uses this feedback to automatically adjust stimulation parameters. This closed-loop feedback mechanism replaces the traditional trial-and-error approach, significantly reducing programming time while maintaining objective optimization of stimulation parameters based on actual brain response
Solution Approach 2:
The programming system performs automatic parameter optimization using algorithms that process measured brain signals and self-adjust stimulation parameters without requiring extensive manual intervention. The system serves itself by automatically identifying optimal parameters based on objective physiological markers, reducing both time and human effort
2Productivity
If automated parameter optimization is implemented, then programming time is reduced, but system complexity increases
Solution Approach 1:
The system replaces manual programming operations with automated computational algorithms that process electrophysiological signals. By substituting mechanical/manual adjustment with automatic signal processing and algorithm-based parameter selection, the system achieves high programming efficiency while the complexity is managed through software rather than hardware complexity
Solution Approach 2:
The system automatically adjusts multiple stimulation parameters (frequency, amplitude, pulse width) based on measured brain responses. By implementing automated parameter optimization across multiple dimensions, the system achieves comprehensive parameter tuning that significantly improves programming efficiency compared to manual methods
3Measurement precision
If multiple contacts on electrode are tested, then optimal implantation site is accurately identified, but measurement and stimulation complexity increases
Solution Approach 1:
The electrode is divided into multiple independent contacts that can be individually stimulated and measured. This segmentation allows systematic testing of different electrode positions and configurations, enabling precise identification of optimal implantation sites by evaluating each contact's effect on HFO and ECA signals separately
Solution Approach 2:
The system dynamically switches between different electrode contacts during programming, adjusting which contacts are active based on real-time measurements. This dynamic reconfiguration allows efficient exploration of multiple electrode positions without requiring permanent changes, achieving high measurement precision while managing complexity through controlled switching
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, personalized optimization of deep brain stimulation parameters by identifying ideal implantation sites and calibrating treatment parameters, enhancing therapeutic effectiveness for movement disorders like Parkinson's disease.
Implementation Method 1
High Frequency Stimulation (HFS) is applied to a contact of a multi-contact electrode
Implementation Method 2
High Frequency Oscillations (HFO) induced in the region of the target structure by the HFS are measured
Data Source
Figure 1
Figure 2
Figure 3
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.