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Localization of subthalamic nucleus from microelectrode recordings with k-means clustering
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Yang, Fan. Localization of subthalamic nucleus from microelectrode recordings with k-means clustering. Retrieved from https://doi.org/doi:10.7282/T31C211T

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TitleLocalization of subthalamic nucleus from microelectrode recordings with k-means clustering
NameYang, Fan (author); Michmizos, Konstantinos (internal member); Pavlovic, Vladimir (internal member); Boularias, Abdeslam (internal member); Rutgers University; School of Graduate Studies
Date Created2017
Other Date2017-10 (degree)
SubjectComputer Science
Extent1 online resource (ix, 31 p. : ill.)
DescriptionBackground: Microelectrode recordings (MERs) of the neural activities are a useful tool for subthalamic nucleus localization (STN) in the process of deep brain stimulation (DBS) surgery for neurological and neuropsychiatric disorders, like Parkinson's disease. Currently, the localization of STN relies on manual demarcation performed by a neurophysiologist. It is also di cult to give an exact detection of the STN borders, especially the one between the STN and the substantia nigra pars reticulata (SNr). As a result, investigators are looking for a multi-feature machine learning method for accurate automated STN localization. We used K-means clustering and a novel set of features that are based on a combination of spike-based and spike independent metrics to address these shortcomings. Methods: We extracted 18 computational features from 31 bandpass ltered (0.5 kHz - 8 kHz) MERs of 20 patients who underwent DBS implantation surgery. 12 of these 18 features were spike-based and the other 6 were spike independent. Square root transformation was performed on those positively skewed features. All features were standardized and normalized to scale [0, 1]. We additionally integrated a depth vector into the original feature matrix. The newly formed feature matrix was fed into the k-means clustering algorithm. K was chosen to be 4 because the trajectories, under most circumstances, went through four consecutive structures in the brain. The STN demarcations were predicted by a combination of k-means clustering and feature activity maps. The results were later compared to the decisions of a human expert. Results: Two of the four newly designed features, i.e. mean inter-spike interval and bursting rate, showed good discriminatory power between STN-SNr region and all the other regions. The modi ed k-means clustering with k = 4 identi ed the STN entry and STN exit with an error of 0:0443 0:2190 and 0:2555+-0:4005, respectively (mean +- standard deviation), and achieved detection sensitivity (error < 0.5 mm) of 93:5% and 80:6%, respectively. Conclusion: The combination of k-means clustering and a novel feature activity map provided a simple and robust method for STN localization. Further integration of the depth information into the original feature matrix has substantial effect in reducing the noise existed in the clustering results given by k-means clustering alone. Since our unsupervised learning approach does not require manual demarcation on its output, the degree of automation has improved. This is expected to lead into a decreased time spent inside the operation room and hopefully improve the clinical outcomes.
NoteM.S.
NoteIncludes bibliographical references
Noteby Fan Yang
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T31C211T
Languageeng
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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