Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals

Successful deep brain stimulation surgery for Parkinson's disease (PD) patients hinges on accurate clustering of the functional regions along the electrode insertion trajectory. Microelectrode recording (MER) is employed as a substantial tool for neuro-navigation and localizing 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 vector 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 features are better suited for identifying STN borders and achieve higher results. Average classification 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.