Closed-Loop Phasic Burst Stimulation via Phase Response Curves
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Solution Overview
Problem
Current deep brain stimulation (DBS) systems for treating Parkinson's disease rely on a time-intensive trial-and-error process to tune stimulation parameters, lacking an effective closed-loop approach to optimize electrical stimulation settings based on individual patient physiology.
Innovation Solution
A closed-loop system that measures electrophysiological activity to estimate phase response curves (PRCs), allowing for the optimization of electrical stimulation parameters and phasic burst stimulation to disrupt pathological oscillations, thereby improving therapeutic efficacy and reducing side effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a trial-and-error process is used to tune stimulation parameters, then stimulation settings can be adjusted, but the process is time-intensive and inefficient
Solution Approach 1:
The system implements a closed-loop feedback mechanism where electrophysiological signals are continuously monitored and fed back to adjust stimulation parameters. The phase response curve analysis provides real-time feedback on the effectiveness of different stimulation phases, enabling automatic optimization without trial-and-error procedures. This feedback loop allows the system to learn from physiological responses and autonomously tune parameters to achieve therapeutic goals.
Solution Approach 2:
The system enables self-service by allowing the stimulation device to automatically determine optimal parameters based on real-time analysis of electrophysiological signals. The embedded algorithms process the recorded signals, compute phase response curves, and adjust stimulation settings without requiring continuous clinician intervention. This self-adjusting capability eliminates the time-intensive manual tuning process while maintaining personalized therapy optimization.
2Reliability
If high frequency stimulation is applied to disrupt pathological oscillations, then therapeutic efficacy is improved, but energy consumption increases and battery life decreases
Solution Approach 1:
The system employs periodic phasic burst stimulation instead of continuous high-frequency stimulation. By delivering stimulation in synchronized bursts at specific phases of the pathological oscillation (determined from phase response curve analysis), the system achieves effective disruption of beta oscillations with significantly reduced overall energy consumption. The periodic nature of the stimulation allows the system to exploit the natural rhythm of the pathology rather than continuously opposing it.
Solution Approach 2:
The system dynamically adjusts stimulation parameters based on real-time physiological state. Rather than applying fixed high-frequency stimulation, the system modulates the timing, amplitude, and duration of stimulation bursts according to the instantaneous phase and amplitude of recorded electrophysiological signals. This dynamic adaptation ensures therapeutic efficacy while minimizing energy expenditure by stimulating only when and where needed.
3Adaptability or versatility
If stimulation parameters are optimized for each patient individually, then therapeutic effectiveness is improved, but the complexity of parameter tuning increases
Solution Approach 1:
The system replaces complex manual parameter tuning with automated computational algorithms. Instead of requiring clinicians to manually adjust multiple parameters based on trial-and-error, the system uses embedded software to automatically compute phase response curves from recorded signals and determine optimal stimulation parameters. This substitution of computational processing for manual adjustment maintains patient-specific customization while dramatically reducing operational complexity.
Solution Approach 2:
The system automatically determines optimal stimulation parameters by analyzing changes in electrophysiological signal characteristics. By computing phase response curves from recorded signals, the system identifies the specific phase and amplitude parameters that will most effectively disrupt pathological oscillations for each patient. This automatic parameter optimization based on physiological parameter changes enables personalized therapy without requiring complex manual tuning procedures.
Data Source
AI summary
Systems and methods are provided for determining optimized settings for a closed-loop stimulation system based on analyzing a phase response curve measured from electrophysiological activity, such as electrical nerve activity or electrical muscle activity. The slope of the phase response curve is computed and used to determine a phase window during which phasic burst stimulation should be provided to achieve a desired effect on oscillations in the subject. When the slope of the phase response curve is positive, a phasic burst stimulation applied during the phase window will decrease synchrony of the oscillations. When the slope of the phase response curve is negative, a phasic burst stimulation applied during the phase window will increase synchrony of the oscillations.