Paper Info
Title
Analysis of microelectrographic neuronal background in deep brain nuclei in Parkinson disease
Abstract
This paper proposes that spectral characteristics of background neuronal potentials can be effective parameters to classifying and identifying neural activities from subthalamic nucleus (STN) and subtantia nigra (SNr). The spike-free background signals were obtained from inter-spike microelectrode recording signals. The averaged periodogram was then used to compute the power spectral density of the background signals. Three spectral parameters were extracted and used as discriminant features for artificial neural networks. The commonly used neuronal firing patterns were also extracted from the detected neuronal spikes and used as discriminant features. Our results showed that the classification performance based on background parameters was similar or better than using neuronal firing patterns. This implied that neuronal background can be useful as an aid in targeting STN as well as neuronal firing patterns, saving from spike identification as single- or multi-neuron discharges.
Year
DOI
Venue
2008
10.1007/978-3-642-02490-0_24
ICONIP (1)
Keywords
Field
DocType
microelectrographic neuronal background,parkinson disease,spectral characteristic,neuronal spike,background neuronal potential,neuronal background,discriminant feature,deep brain nucleus,background parameter,spike-free background signal,neuronal firing pattern,power spectral density,background signal,artificial neural network
Deep brain stimulation,Pattern recognition,Computer science,Spectral density,Periodogram,Artificial intelligence,Artificial neural network,Subthalamic nucleus,Machine learning,Microelectrode
Conference
Volume
ISSN
ISBN
5506
0302-9743
3-642-02489-0
Citations 
PageRank 
References 
0
0.34
1
Authors
6
Name
Order
Citations
PageRank
Hsiao-Lung Chan117619.98
Ming-An Lin292.22
Tony Wu351.20
Pei-Kuang Chao4132.25
Shih-Tseng Lee5113.58
Peng-Chuan Chen600.34