A Metric for Evaluating and Comparing Closed-Loop Deep Brain Stimulation Algorithms

TL;DR

This paper introduces a new metric for evaluating closed-loop deep brain stimulation algorithms, enabling better comparison of their performance across patients by balancing tremor control and power efficiency.

Contribution

A novel normalized performance metric for closed-loop DBS algorithms is proposed, facilitating standardized evaluation and comparison across different patient cases.

Findings

The metric varies per patient and algorithm, reflecting individual differences.

It balances tremor reduction with power savings effectively.

The metric can serve as a benchmark for future algorithm development.

Abstract

Objective: Closed-loop deep brain stimulation (DBS) may improve current clinical DBS treatment for neurological movement disorders, but control algorithms may perform differently across patients. New metrics are needed for comparing and evaluating closed-loop algorithm performance that address the specific needs of closed-loop neuromodulation controllers. Approach: A metric is proposed for system performance that includes normalized terms that can be used to compare algorithm performance for a patient. This metric was evaluated using two closed-loop control algorithms that were tested in patients with Parkinson's Disease (PD) who experience rest tremor. Main Results: The metric's resulting balance between tremor treatment and power savings varied on a per patient and algorithm basis. This was expected given how each trial resulted in a variable reduction in stimulation power at the cost of additional tremor for the patient when compared to open-loop stimulation. Significance: The proposed metric will aid in clinical evaluation of new algorithms and provide a benchmark for future system designers. This will be important given the growing potential applications of dynamically adjusted neural stimulation. ClinicalTrials.gov Identifier: NCT02384421.