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

Abstract EN

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.