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Ass. Lect. Mohamed Hosny Ali Mohamed Elnogomy :: Publications:

Title:
Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals.
Authors: Mohamed Hosny; Minwei Zhu; Wenpeng Gao; Yili Fu
Year: 2021
Keywords: Higher-order statistics and spectra; Microelectrode recording; Parkinson's disease; subthalamic nucleus detection; Deep brain stimulation; Machine learning
Journal: Biocybernetics and Biomedical Engineering
Volume: 41
Issue: 2
Pages: 704 - 716
Publisher: Elsevier
Local/International: International
Paper Link:
Full paper Mohamed Hosny _Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals.pdf
Supplementary materials Not Available
Abstract:

Successful deep brain stimulation surgery for Parkinson’s disease (PD) patients hinges on accurate clustering of the functional regions along the electrode insertion trajectory. Micro- electrode recording (MER) is employed as a substantial tool for neuro-navigation and local- izing the optimal target, such as the subthalamic nucleus (STN), intraoperatively. MER signals deliver a framework to reveal the underlying characteristics of STN. The motivation behind this work is to explore the application of Higher-order statistics and spectra (HOS) for an automated delineation of the neurophysiological borders of STN using MER signals. Database collected from 21 PD patients were used. Two HOS methods (Bispectrum and cumulant) were exploited to probe non-Gaussian properties of STN region. This is followed by utilizing classifiers, namely K-nearest neighbor, decision tree, Boosting and support vec- tor machine (SVM), to choose the superior classifier. Comparison of the performance achieved via HOS alongside the state-of-the-art techniques shows that the proposed fea- tures are better suited for identifying STN borders and achieve higher results. Average clas- sification accuracy, sensitivity, specificity, area under the curve and Youden’s J statistics of 94.81%, 96.73%, 92.15%, 0.9444% and 0.8888, respectively, were yielded using SVM with 8 bispectrum and 241 cumulants features. The proposed model can aid the neurosurgeon in STN detection.

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