Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy

Abstract

Most of the current complex network studies about epilepsy used the electroencephalogram (EEG) to directly construct the static complex network for analysis and discarded the dynamic characteristics. This study constructed the dynamic complex network on EEG from pediatric epilepsy and pediatric control when they were asleep by the sliding window method. Dynamic features were extracted and incorporated into various machine learning classifiers to explore their classification performances. We compared these performances between the static and dynamic complex network. In the univariate analysis, the initially insignificant topological characteristics in the static complex network can be transformed to be significant in the dynamic complex network. Under most connectivity calculation methods between leads, the accuracy of using dynamic complex network features for discrimination was higher than that of static complex network features. Particularly in the imaginary part of the coherency function (iCOH) method under the full-frequency band, the discrimination accuracies of most machine learning classifiers were higher than 95%, and the discrimination accuracies in the higher-frequency band (beta-frequency band) and the full-frequency band were higher than that of the lower-frequency bands. Our proposed method and framework could efficiently summarize more time-varying features in the EEG and improve the accuracies of the discrimination of the machine learning classifiers more than using static complex network features.

Diagnosis for autism spectrum disorder based on electroencephalogram dynamic local graph theory indices

“…Here the width for window was set in 1s, 3s, 5s, respectively, and the moving step was set to be 50% or 100% of the window width. Here we utilize 6 types of dynamic indices, namely the mean, standard deviation, median, inter-quartile range, kurtosis and skewness of the node strength [25] to describe the dynamic traits along the extracted windows. Like static node strength index, for each of 6 dynamic graph theory indices, 5 (frequency subband number) × 19 (EEG channel number) = 95 features can be calculated for each subject.…”

“…Totally 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2) were placed onto the scalp of the patient according to the international 10-20 montage system with reference channels being Fz, Cz and Pz. The preprocessing steps of EEG were as follows: (1) band-pass ltering between 0.1Hz-45Hz was conducted for EEG signals to eliminate artifacts; (2) independent component analysis (ICA) was performed to remove artifacts derived from eyes and muscle movement; (3) manually removing the bad segments which can not be identi ed by ICA; (4) the 100s segments that included as few artifacts as possible were selected by experts; (5) nally, EEG were further ltered into 5 frequency subbands, namely Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha-1 (8-10 Hz), Alpha-2 (10-12 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The preprocess mentioned above were conducted by EEGLab toolbox [23] in Matlab v. 2019a.…”

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

“…Measures such as the clustering coefficient, path length, and efficiency are usually used to characterize the system of functional brain networks at local and global levels [ 34 , 35 ]. When the average path length is shorter and the average clustering coefficient is larger, the information processing and transmission is faster [ 29 ].…”