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Artifact Characterization and a Multipurpose Template-Based Offline Removal Solution for a Sensing-Enabled Deep Brain

Lauren H Hammer1, Ryan B Kochanski2, Philip A Starr2

  • 1Department of Neurology, University of California, San Francisco, San Francisco, California, USA.

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|February 7, 2022
PubMed
Summary

Artifacts in deep brain stimulation (DBS) signals from the Medtronic Percept device were identified and mitigated using template subtraction. This method successfully removed ECG and stimulation artifacts, crucial for developing adaptive DBS (aDBS).

Keywords:
Adaptive deep brain stimulationArtifact removalDeep brain stimulationElectrical stimulationLocal field potentials

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Devices

Background:

  • Medtronic Percept is the first FDA-approved deep brain stimulation (DBS) device with active stimulation sensing.
  • Real-world signal-recording properties of the Percept device require further description.

Purpose of the Study:

  • To identify and mitigate artifacts in local field potential (LFP) signals from the Percept DBS device.
  • To assess the impact of artifacts on adaptive DBS (aDBS) development.

Main Methods:

  • Collected LFP signals from 7 subjects in experimental and clinical settings.
  • Evaluated artifacts and their spectral impact in stimulation ON/OFF states.
  • Applied three offline artifact removal techniques: template subtraction, singular value decomposition, and QRS interpolation.

Main Results:

  • Template subtraction effectively removed ECG, polyphasic, and ramping artifacts.
  • ECG removal resulted in spectral shapes similar to OFF stimulation.
  • Singular value decomposition was effective but required subjective input; QRS interpolation left residual artifacts.

Conclusions:

  • Artifacts during active stimulation significantly alter Percept LFP signal properties.
  • Automated template subtraction successfully removed discrete artifacts offline.
  • Unrejected artifacts can impact online power estimates and aDBS algorithm performance.