Closed-loop DBS System Using RF Power and LFP Feedback

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Solution Overview

Problem

Current deep brain stimulation (DBS) systems for treating neurological disorders like essential tremor, dystonia, and Parkinson's disease rely on open-loop methods that require frequent manual adjustments by neurologists or technicians, which are inefficient and power-intensive, especially when using spike signals, while local field potential (LFP) signals, a more effective indicator, are not fully utilized due to power constraints.

Innovation Solution

A closed-loop DBS system that measures and processes both LFP and spike signals using a microprocessor to automatically adjust stimulation parameters, incorporating a logarithmic analog-to-digital converter, digital filters, and a programmable PI-controller to generate optimized stimulation signals, powered by a radio frequency-DC converter module, eliminating the need for batteries and reducing power consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If spike signals are used with principal component analysis for DBS parameter adjustment, then the system can disentangle spike signals from each other and background noise, but the computation becomes extremely intensive making long-term battery or RF power supply infeasible

Engineering Contradiction:

Improvespike signal measurement precisionVSAvoidpower consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent extracts and utilizes only the essential features of neural signals (LFP power spectral density in specific frequency bands) rather than performing complete spike signal decomposition. This selective extraction of relevant information reduces computational complexity while maintaining clinical effectiveness for DBS parameter adjustment.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent employs simplified signal processing algorithms that require minimal computational resources, analogous to using disposable low-cost solutions. The processing can be performed with simple microcontrollers rather than high-performance processors, making the system feasible for implantable devices with limited power supply.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

2Measurement precision

If LFP signals are used for DBS feedback, then the system achieves more effective feedback indication, but current closed-loop approaches are too power intensive to be feasible as long-term RF or battery-powered solutions

Engineering Contradiction:

Improvefeedback indication effectivenessVSAvoidpower consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent implements partial processing of LFP signals by focusing only on calculating power spectral density in specific clinically relevant frequency bands (beta band for Parkinson's, gamma band for essential tremor) rather than analyzing the complete frequency spectrum. This partial analysis approach maintains feedback effectiveness while dramatically reducing computational power requirements.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent changes the processing parameters from complete time-domain or frequency-domain analysis to simplified power spectral density estimation in specific bands. This parameter change reduces the computational burden while preserving the clinically relevant information needed for effective closed-loop DBS control.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If open-loop DBS with manual parameter adjustment is used, then the system can be implemented with simple hardware, but the process requires frequent 3-5 hour visits to neurologists or technicians for parameter adjustment

Engineering Contradiction:

Improvesystem complexityVSAvoidtime for parameter adjustment

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The patent implements a closed-loop feedback system where LFP signals are continuously monitored and used to automatically adjust DBS parameters. The system calculates power spectral density in real-time and adjusts stimulation parameters based on the feedback from neural activity, eliminating the need for frequent manual adjustments by clinicians.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent enables the DBS system to self-adjust its parameters based on real-time neural signal analysis. The implanted device autonomously processes LFP signals and modifies stimulation parameters without requiring external intervention, making the system self-sufficient and eliminating repeated clinical visits.

Inventive Principle:

Principle #25Self-service

4Adaptability or versatility

If frequent manual parameter adjustments are performed using external components, then the system can adapt to changing symptoms, but the process is inefficient and requires the patient to return frequently for 3-6 month intensive adjustment periods

Engineering Contradiction:

Improvetreatment adaptabilityVSAvoidtreatment efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements continuous feedback from LFP signals that enables real-time adaptation of DBS parameters. The system automatically detects changes in neural activity patterns and adjusts stimulation parameters accordingly, providing continuous adaptability without requiring periodic manual re-adjustment sessions.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent ensures continuous monitoring and adjustment of DBS parameters through real-time LFP signal processing. The closed-loop system operates continuously to maintain optimal treatment parameters, eliminating the intermittent nature of manual adjustments and providing continuous therapeutic adaptation.

Inventive Principle:

Principle #20Continuity of useful action

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

Enables efficient, long-term, battery-free operation of DBS devices by leveraging LFP signals for personalized treatment, reducing the need for frequent manual adjustments and improving treatment efficacy for neurological disorders.

Implementation Method 1

encoding the amplified signals in the digital logarithmic domain via a logarithmic analog-to-digital converter module

Methodology Applied

Scientific EffectAnalog-to-digital conversion:

Implementation Method 2

filtering in the log domain, via a logarithmic digital filter module, the received neural signals

Methodology Applied

Scientific EffectDigital filtering: Filter (electronic)

Implementation Method 3

Methodology Applied

Scientific EffectRadio frequency to DC conversion:

Data Source

PatentUS8892208B2Closed-loop neural stimulation

Publication Date: 2014.11.18 THE RGT UNIV OF MICHIGAN

AI summary

A system and method for conducting closed loop deep brain stimulation on an individual, and more specifically, for receiving local field potential neural signals, encoding and filtering the signals into the logarithmic domain, processing the signals, and determining optimal stimulation parameters for deep brain stimulation based on the processed neural signals. The system and method may also include an RF-DC converter such that the system may be powered in whole or in part based on radio frequency signals. The system and method may also include an RF transceiver such that the system may transmit data wirelessly to an external receiver, or may receive stimulation parameters wirelessly from an external transceiver.