Efficient artifact removal for adaptive deep brain stimulation and a temporal event localization analysis

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 advance 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 limits its use in real-time applications. To address this, we developed SMARTA+, a computationally efficient extension of SMARTA capable of suppressing both stimulus artifacts and DC transient artifacts while supporting flexible algorithmic design.

We evaluated SMARTA+ using semi-real aDBS data and real data from Parkinson's disease patients. It preserved the spectral and temporal structure of the underlying LFPs and demonstrated robustness across a variety of simulated stimulation protocols. Furthermore, the temporal event localization analysis showed that SMARTA+ can accurately determine the beta-burst events.

SMARTA+ outperformed template subtraction, pulse blanking, and transient blanking, and achieved performance comparable to or better than SMARTA while substantially reducing computation time.

By enhancing artifact suppression and improving computational efficiency, we show that SMARTA+ has the potential to advance real-time, closed-loop aDBS systems for neuromodulation therapies across diverse neurological disorders.