DBS Parameter Tuning via Automated Movement Quantification
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Solution Overview
Problem
Current deep brain stimulation (DBS) procedures for treating movement disorders like Parkinson's disease are inefficient due to a lack of tools that combine physiological, electrical, and behavioral data for optimal electrode placement and stimulation parameter adjustment, leading to lengthy and costly programming sessions with subjective assessments.
Innovation Solution
A system that uses sensors to measure movement data, processes kinematic features, and applies a trained algorithm to objectively quantify movement disorder symptoms, providing real-time guidance for adjusting DBS parameters and reducing the need for extensive clinician expertise and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual programming sessions are used to adjust DBS parameters, then clinician control and customization are improved, but time consumption and procedural cost increase
Solution Approach 1:
The system performs automated symptom quantification and parameter optimization without requiring continuous clinician intervention. The processor automatically analyzes movement data, quantifies symptoms, and determines optimal DBS parameters, allowing the system to serve itself in the tuning process while the clinician retains oversight capability.
Solution Approach 2:
The patent replaces the manual mechanical process of clinician assessment and parameter adjustment with an automated computational system. Sensors capture movement data, processors analyze kinematic features, and algorithms optimize parameters, substituting the manual clinician workflow with an automated information processing system that reduces time while maintaining control.
2Manufacturing precision
If extensive programming sessions are conducted to optimize DBS parameters, then treatment precision is improved, but procedural cost and patient burden increase
Solution Approach 1:
The system implements closed-loop feedback by continuously monitoring movement data through sensors, quantifying symptom severity, and using this information to automatically adjust DBS parameters. This feedback mechanism enables precise parameter optimization without requiring multiple lengthy programming sessions, as the system self-corrects based on real-time symptom assessment.
Solution Approach 2:
The system automatically adjusts multiple DBS parameters (amplitude, pulse width, frequency, contact configuration) based on quantified symptom severity. By systematically varying these parameters and evaluating their effect on movement symptoms through automated analysis, the system achieves precise optimization without increasing procedural complexity for the patient.
3Productivity
If automated algorithms are used to adjust DBS parameters, then procedural efficiency is improved, but reliance on subjective clinician assessment decreases
Solution Approach 1:
The system introduces an intermediary computational layer between the patient's movement symptoms and the DBS parameter adjustment. Sensors capture objective movement data, processors analyze kinematic features, and algorithms translate this information into parameter recommendations. This intermediary system preserves and utilizes clinician expertise by encoding it in trained algorithms while eliminating subjective assessment variability.
Solution Approach 2:
The system creates a computational model that copies and codifies clinician expertise into trained algorithms. By training machine learning models on expert clinician decisions and outcomes, the system replicates their judgment capabilities in an automated format, maintaining the value of clinical expertise while enabling efficient automated operation.
Data Source
AI summary
A system and method for tuning the parameters of a therapeutic medical device comprises a movement measurement data acquisition system capable of wireless transmission; processing comprising kinematic feature extraction, a scoring algorithm trained using scores from expert clinicians, a therapeutic device parameter setting adjustment suggestion algorithm preferably trained using the parameter setting adjustment judgments of expert clinicians; and a display and/or means of updating the parameter settings of the treatment device. The invention facilitates the treatment of movement disorders including Parkinson's disease, essential tremor and the like by optimizing deep brain stimulation (DBS) parameter settings, eliminating as much as possible motor symptoms and reducing time and costs of surgical and outpatient procedures and improving patient outcomes. In preferred embodiments, the system provides recommendations for treatment which may be semi-automatically or automatically applied to update the parameter settings of a treatment device such as a DBS implant.