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A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients
- Source :
- Complexity, Vol 2020 (2020)
- Publication Year :
- 2020
- Publisher :
- Hindawi Limited, 2020.
-
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.
- Subjects :
- medicine.medical_specialty
Article Subject
General Computer Science
Computer science
Intracranial Electroencephalography
Signal
030218 nuclear medicine & medical imaging
03 medical and health sciences
Epilepsy
0302 clinical medicine
medicine
Intracranial electrodes
Multidisciplinary
Artificial neural network
medicine.diagnostic_test
Oscillation
business.industry
Detector
Pattern recognition
QA75.5-76.95
Magnetoencephalography
medicine.disease
Electronic computers. Computer science
Neurosurgery
Artificial intelligence
business
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 10990526 and 10762787
- Volume :
- 2020
- Database :
- OpenAIRE
- Journal :
- Complexity
- Accession number :
- edsair.doi.dedup.....3abdd8031fc6df97cac6de86b7416e78