Towards an objective, standardized beta frequency peak detection algorithm to inform adaptive deep brain stimulation programming in Parkinson’s disease

Author:

Thompson John1ORCID,Baker Sunderland2,Radcliffe Erin1,Kramer Daniel1,Ojemann Steven1,Case Michelle3,Zarns Caleb4,Holt-Becker Abbey4,Raike Robert3,Baumgartner Alexander1ORCID,Kern Drew1

Affiliation:

1. University of Colorado Anschutz Medical Campus

2. Penn State University

3. Medtronic PLC

4. Medtronic

Abstract

Abstract

Oscillatory activity within the beta frequency range (13-30Hz) serves as a Parkinson’s disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms, both existing and new, against a benchmark established by expert consensus. The most accurate algorithms matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.

Publisher

Springer Science and Business Media LLC

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