A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients

Jun Liu, Siqi Sun, Yang Liu, Jiayang Guo, Hailong Li, Yuan Gao, Jintao Sun and Jing Xiang

Complexity, 2020, vol. 2020, 1-9

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9237808

DOI: 10.1155/2020/9237808

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