ArticleNPJ Parkinson's disease2024
Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson's disease.
Article in NPJ Parkinson's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Inconsistent subthalamic local field potential beta activity amid in- and antiphasic neuronal bursts.NPJ Parkinson's disease · 2026Article
- Subthalamic beta activity and neuroimaging concordance in deep brain stimulation: electrode placement and clinical outcomes.Clinical neurophysiology practice · 2026Article
- Proceedings of the 13th annual deep brain stimulation think tank: the evolving landscape.Frontiers in human neuroscience · 2026Review
- Beta Power Response After Levodopa Intake in Parkinson's Disease Patients With Chronic Sensing-Enabled Deep Brain Stimulation.Annals of neurology · 2025Article
- Complexity of fractal dimension patterns and machine learning-based classification of altered motor cortical oscillatory activity in rodent models of Parkinson disease.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oscillatory activity within the beta frequency range (13-30 Hz) 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 against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, 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.
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Registered trials
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