关键词: 静息态脑磁图, 脑功能网络, 机器学习, 特征提取

Abstract:

Magnetoencephalography（MEG） is now widely used in clinical examination and medical research in many fields. Resting-state-based MEG brain network analysis can be used to study the physiological or pathological mechanism of the brain. Magnetoencephalogram analysis has important reference value for the diagnosis of epilepsy. The automatic classification of epileptic magnetoencephalic signals can make timely judgments about the patient’s condition, which is of great clinical significance. The existing literature on the automatic classification of epileptic EEG signals has been fully studied, but the study of epileptic MEG signals is relatively weak. In this paper, an automatic discrimination method of epileptic magnetoencephalic spike wave signals based on full-band machine learning based on brain functional connectivity network is proposed. Four classifiers are synthetically discriminated and compared, and the classifier with the best effect is selected. The discriminant accuracy can reach 93.8%. Therefore, this method has promising application prospects in automatic recognition and marking of epileptic spikes in MEG.

Key words: resting state Magnetoencephalography（MEG）, brain function network, machine learning, feature extraction