Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy
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 cont...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2553 |
| _version_ | 1797437744684728320 |
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| author | Zichao Liang Siyang Chen Jinxin Zhang |
| author_facet | Zichao Liang Siyang Chen Jinxin Zhang |
| author_sort | Zichao Liang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:27:03Z |
| format | Article |
| id | doaj.art-e7b455594f6f4930be3bf4d823613599 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:27:03Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e7b455594f6f4930be3bf4d8236135992023-12-01T00:00:42ZengMDPI AGSensors1424-82202022-03-01227255310.3390/s22072553Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric EpilepsyZichao Liang0Siyang Chen1Jinxin Zhang2Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, ChinaDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, ChinaDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, ChinaMost 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.https://www.mdpi.com/1424-8220/22/7/2553dynamic complex networkfeature extractionsliding window analysisEEGpediatric epilepsy |
| spellingShingle | Zichao Liang Siyang Chen Jinxin Zhang Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy Sensors dynamic complex network feature extraction sliding window analysis EEG pediatric epilepsy |
| title | Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy |
| title_full | Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy |
| title_fullStr | Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy |
| title_full_unstemmed | Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy |
| title_short | Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy |
| title_sort | feature extraction of the brain s dynamic complex network based on eeg and a framework for discrimination of pediatric epilepsy |
| topic | dynamic complex network feature extraction sliding window analysis EEG pediatric epilepsy |
| url | https://www.mdpi.com/1424-8220/22/7/2553 |
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