Research Area: Machine Learning
High-frequency oscillations (HFOs) are spontaneous electroencephalogram patterns that have been regarded as potential biomarkers of epileptic seizure onset zones (SOZs). Accurately detected HFOs are used to localize SOZs, which is crucial for the presurgical assessment. Since the visual marking of HFOs is time-consuming, a method is desirable to automatically detect HFOs for localizing SOZs in clinical practice. However, the existing methods cannot obtain satisfactory performance, which are not suitable for clinical application. In order to solve this problem, we present a new localization method for epileptic SOZs in this study. Firstly, a threshold method is used to detect events of interest (EoIs). Secondly, a time-frequency analysis method is adopted to acquire channels of interest (CoIs) by calculating the average power of EoIs on each channel. Then, the k-medoids clustering method is employed to detect HFOs of CoIs. Finally, the concentrations of detected HFOs are used to localize SOZs. The superiority of our localization method is demonstrated by comparing its sensitivity and specificity with some existing methods.
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Author(s) Name: Min Wu, Ting Wan, Xiongbo Wan, Zelin Fang, Yuxiao Du
Journal name: Pattern Recognition
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.patcog.2020.107687
Volume Information: Volume 111, March 2021, 107687
