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.
Highlights
Epilepsy is a spectrum of neurological disorders, caused by the abnormal firing of neurons in the brain with sudden and recurrent characteristics
We investigated three dimension reduction methods, including principal component analysis (PCA), Kernel Principal Component Analysis (KPCA), and Local Linear Embedding (LLE): (1) PCA is a multivariate analysis technique in which dependent variables are determined by the values of several independent variables
In each repetition of the experiment, we evaluated true positive (TP), false positive (FP), true negative (TN), false negative (FN), and true positive rate (TPR) for the classification by comparing the predicted labels and true labels
Summary
Epilepsy is a spectrum of neurological disorders, caused by the abnormal firing of neurons in the brain with sudden and recurrent characteristics. It has tremendous adverse impacts to the epileptic patients. Many previous researches explored the pathogenesis of epilepsy from the cellular level to the molecular level and the gene level [1]. Neurosurgery is often required to gain seize freedom [2]. A successful epileptic surgery highly depends on accurate localization of the origin of epileptic foci, the areas of brain cortex generating the epileptic seizures and understanding postoperative changes in epilepsy network. Localization of epileptic foci is usually very challenging. Invasive surgical intracranial electroencephalography (iEEG) with intracranial electrode placement has been used before neurologic surgery
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
