A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients. (4th September 2020)

• Record Type: Journal Article
• Authors: Liu, Jun; Sun, Siqi; Liu, Yang; Guo, Jiayang; Li, Hailong; Gao, Yuan; Sun, Jintao; Xiang, Jing
• Other Names: Yang Zhile Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-09-04
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/9237808
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Epilepsy is a neurological disease, and the location of a lesion before neurosurgery or invasive intracranial electroencephalography (iEEG) surgery using intracranial electrodes is often very challenging. The high-frequency oscillation (HFOs) mode in MEG signal can now be used to detect lesions. Due to the time-consuming and error-prone operation of HFOs detection, an automatic HFOs detector with high accuracy is very necessary in modern medicine. Therefore, an optimized capsule neural network was used, and a MEG (magnetoencephalograph) HFOs detector based on MEGNet was proposed to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first time that a neural network has been used to detect HFOs in MEG. After optimized configuration, the accuracy, precision, recall, and F1-score of the proposed detector reached 94%, 95%, 94%, and 94%, which were better than other classical machine learning models. In addition, we used the k-fold cross-validation scheme to test the performance consistency of the model. The distribution of various performance indicators shows that our model is robust.