Efficient Artifacts Removal for Adaptive Deep Brain Stimulation and a Temporal Event Localization Analysis

TL;DR

This paper introduces SMARTA+, a computationally efficient artifact removal algorithm for adaptive deep brain stimulation that effectively suppresses artifacts, preserves neural signals, and enhances real-time closed-loop neuromodulation.

Contribution

We developed SMARTA+, an extension of SMARTA, capable of real-time artifact suppression in aDBS, handling transient DC artifacts with improved efficiency and flexibility.

Findings

SMARTA+ achieves comparable or better artifact removal than existing methods.

It significantly reduces computational time, enabling real-time application.

Enhanced detection of beta bursts improves event localization accuracy.

Abstract

Adaptive deep brain stimulation (aDBS) leverages symptom-related biomarkers to deliver personalized neuromodulation therapy, with the potential to improve treatment efficacy and reduce power consumption compared to conventional DBS. However, stimulation-induced signal contamination remains a major technical barrier to advancing its clinical application. Existing artifact removal strategies, both front-end and back-end, face trade-offs between artifact suppression and algorithmic flexibility. Among back-end algorithms, Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA) has shown promising performance in mitigating stimulus artifacts with minimal distortion to local field potentials (LFPs), but its high computational demand and inability to handle transient direct current (DC) artifacts limit its use in real-time applications. To address this, we developed SMARTA+, a computationally efficient extension of SMARTA capable of suppressing both stimulus and transient DC artifacts while supporting flexible algorithmic design. We evaluated SMARTA+ using semi-real aDBS data and real data from Parkinson's disease patients. Compared to SMARTA and other established methods, SMARTA+ achieved comparable or superior artifact removal while significantly reducing computation time. It preserved spectral and temporal structures, ranging from beta band to high-frequency oscillations, and demonstrated robustness across diverse stimulation protocols. Temporal event localization analysis further showed improved accuracy in detecting beta bursts. These findings support SMARTA+ as a promising tool for advancing real-time, closed-loop aDBS systems.