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

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Adaptive deep brain stimulation (aDBS) offers personalized neuromodulation but faces challenges with stimulation-induced signal contamination.
• Current artifact removal methods present trade-offs between effectiveness and flexibility, limiting real-time applications.

Purpose of the Study:

• To develop a computationally efficient algorithm, SMARTA+, for robust artifact removal in adaptive deep brain stimulation (aDBS).
• To address limitations of existing methods, including handling transient direct current (DC) artifacts and improving real-time processing capabilities.

Main Methods:

• Developed SMARTA+, an enhanced version of the Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA) algorithm.
• Evaluated SMARTA+ using simulated and real-world aDBS data from Parkinson's disease patients, assessing artifact suppression and computational performance.

Main Results:

• SMARTA+ effectively suppressed both stimulus and DC transient artifacts while preserving the spectral and temporal characteristics of local field potentials (LFPs).
• The algorithm demonstrated robustness across various stimulation protocols and outperformed existing methods like template subtraction and pulse blanking.
• Achieved performance comparable to or better than SMARTA with significantly reduced computation time.

Conclusions:

• SMARTA+ enhances artifact suppression and computational efficiency, making it suitable for real-time, closed-loop aDBS systems.
• This advancement holds potential for improving neuromodulation therapies across a range of neurological disorders.