脑深部刺激
帕金森病
BETA(编程语言)
刺激
疾病
计算机科学
人工智能
神经科学
心理学
算法
医学
内科学
程序设计语言
作者
John A. Thompson,Sunderland Baker,Erin Radcliffe,Daniel R. Kramer,Steven Ojemann,Michelle Case,Caleb Zarns,Abbey Holt-Becker,Robert S. Raike,Alexander J. Baumgartner,Drew S. Kern
出处
期刊:Research Square - Research Square
日期:2024-03-22
标识
DOI:10.21203/rs.3.rs-3994762/v1
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI