Periodic Artifact Removal With Applications to Deep Brain Stimulation

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Deep brain stimulation (DBS) is effective for neurological disorders like Parkinson's disease.
• Adaptive DBS requires analyzing neural signals during stimulation to identify biomarkers.
• High-amplitude stimulation artifacts obscure neural signals, hindering analysis.

Purpose of the Study:

• To develop a novel algorithm for removing DBS stimulation artifacts.
• To enable real-time analysis of neural signals for adaptive DBS systems.
• To facilitate biomarker discovery by improving signal quality.

Main Methods:

• A novel periodic artifact removal algorithm was developed.
• The algorithm handles missing data and high stimulation frequencies.
• Numerical examples were used to validate the algorithm's performance.

Main Results:

• The algorithm accurately removes DBS stimulation artifacts.
• It functions effectively even with missing data.
• It can handle stimulation frequencies exceeding the Nyquist frequency.

Conclusions:

• The proposed algorithm can be implemented in embedded closed-loop DBS therapies.
• It aids in real-time artifact removal for biomarker discovery.
• This facilitates a deeper understanding of DBS mechanisms and optimization.