Feature Detection of Subthalamic Local Field Potentials in Parkinson's Disease
Zhang Kun
Du Xueying
Huang Yongzhi
Luo Huichun
Wang Shouyan
Abstract：Objective To detect dynamically the characteristics of subthalamic local field potentials (LFPs) related to treatment of Parkinson's disease.Methods The synchronization and patterning characteristics of subthalamic LFPs were analyzed and measured based on wavelet packet transform.The dynamic threshold model was developed to dynamically determine the functional neural states of the subthalamic nucleus.The performance of the detection approach was evaluated and the parameters were optimized with simulated signals.The optimized detection of the characteristic neural oscillations was compared before and after medication.Results The patterning characteristics with high amplitude,low randomness and high regularity were found in the beta band of subthalamic LFPs.The comparison of the patients off and on medication conditions showed that the drug had significant influence on LFPs dynamic characteristics.The total time,frequency,duration and amplitude of the signal with the patterning characteristics significantly decreased after taking medication.Conclusion The method proposed in this paper can dynamically detect the characteristics of subthalamic LFPs in Parkinson's disease and quantify the state of the brain's function.Moreover,this study provides a brain state identification approach based on synchronization features for adaptive deep brain stimulation.
Keywords：Parkinson's diseaselocal field potentialswavelet packetdynamic detectionclosed-loop deep brain stimulation
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 208-213 )