Deep Brain Stimulation Controller Using Beta Burst Feedback
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Solution Overview
Problem
Current deep brain stimulation (DBS) systems for treating movement disorders like Parkinson's disease are often 'open-loop' and lack real-time adaptability, failing to effectively manage symptoms such as freezing of gait, which can be resistant to medication and require invasive manual adjustments.
Innovation Solution
Implementing a closed-loop DBS system that uses beta burst feedback to classify neural activity signals and modify stimulation parameters in real-time, including filtering neural signals to identify beta bursts and adjusting stimulation intensity based on their classification as normal or pathological, using a stimulation map to guide these changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If open-loop DBS systems are used to treat movement disorders, then the system structure is simple, but the system lacks real-time adaptability and cannot effectively manage symptoms like freezing of gait
Solution Approach 1:
The patent implements a closed-loop DBS system that uses beta burst detection from local field potential signals as feedback to automatically adjust stimulation parameters. The system detects pathological beta bursts in real-time and modifies stimulation delivery accordingly, providing the needed real-time adaptability while managing complexity through automated feedback control
Solution Approach 2:
The system dynamically adjusts stimulation parameters based on real-time detection of beta burst characteristics. By making the stimulation parameters variable and adaptive rather than fixed, the system achieves real-time adaptability to changing neural activity patterns and symptom states
2Ease of operation
If manual adjustments are made to DBS parameters to manage freezing of gait, then treatment can be customized, but the process is time-consuming and requires invasive intervention
Solution Approach 1:
The DBS system performs self-adjustment by automatically detecting beta bursts and modifying stimulation parameters without requiring manual intervention. The automated closed-loop control enables the system to adapt to symptom changes independently, eliminating the need for time-consuming manual adjustments while maintaining personalized treatment
3Reliability
If continuous DBS stimulation is provided to treat PD symptoms, then therapeutic benefit is maintained, but energy consumption is high and cannot respond to symptom variations
Solution Approach 1:
The system uses periodic detection of beta bursts to trigger stimulation adjustments only when needed. Rather than continuous monitoring and adjustment, the system periodically assesses neural activity and modifies stimulation based on detected pathological patterns, reducing energy consumption while maintaining therapeutic effectiveness
Solution Approach 2:
The system changes stimulation parameters dynamically based on detected beta burst characteristics. By adjusting parameters such as amplitude, frequency, or pulse width in response to pathological beta bursts, the system maintains reliable therapeutic benefit while consuming less energy compared to continuous fixed-parameter stimulation
4Measurement precision
If beta burst detection is implemented to classify neural activity, then real-time symptom monitoring is achieved, but signal processing complexity increases
Solution Approach 1:
The system extracts specific beta burst features from local field potential signals that are most relevant for detecting pathological activity. By focusing on key characteristics such as frequency range (13-30 Hz), duration, and power thresholds, the system achieves precise neural activity classification while minimizing unnecessary signal processing complexity
Data Source
AI summary
Systems and methods for deep brain stimulation using beta burst feedback in accordance with embodiments of the invention are illustrated. One embodiment includes a deep brain stimulation system, including a neurostimulator, and a controller, where the controller is communicatively coupled to the neurostimulator and configured to obtain a plurality of neural activity signals from the neurostimulator, identify beta bursts within each neural activity signal, classify identified beta bursts as pathological or normal, and modify stimulation provided by the neurostimulator based on the classified beta bursts.