Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Complexity (New York, N.Y.), 2020, Vol.2020, p.1-9

Ort / Verlag

Hoboken: Hindawi

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.