Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm
Neonatal brain injury carries the risk of neurological sequelae such as epileptic seizures, cerebral palsy, intellectual disability, and even death. Classification methods based on electroencephalography (EEG) signals and machine learning algorithms are crucial for assessing neonatal brain injury. H...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-07-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-3000.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849703710912937984 |
|---|---|
| author | Ling Li Tao Yue Hui Wu Yanping Zhao Qinmei Liu Hairong Zhang Wei Xu |
| author_facet | Ling Li Tao Yue Hui Wu Yanping Zhao Qinmei Liu Hairong Zhang Wei Xu |
| author_sort | Ling Li |
| collection | DOAJ |
| description | Neonatal brain injury carries the risk of neurological sequelae such as epileptic seizures, cerebral palsy, intellectual disability, and even death. Classification methods based on electroencephalography (EEG) signals and machine learning algorithms are crucial for assessing neonatal brain injury. However, classification methods that utilise all features from the original EEG signals may result in lengthy training and classification times, thereby reducing the performance of the classification system. This article presents a novel classification system with a proposed feature selection method for assessing neonatal brain injury, in which the feature selection method is combined using elastic net (EN) regression and an improved crow search algorithm (ICSA), named EN-ICSA. In the EN-ICSA method, EN regression is used to conduct the pre-screening of features. The ICSA is utilised to select the essential figures further by introducing the dynamic perception probability for deciding whether to search locally or globally, a novel neighbor-following strategy for the local search and a global search strategy according to the crow’s search experience, resulting in accelerating the search efficiency while effectively avoiding falling into local optima. Experimental results demonstrate that the proposed system, based on support vector machine (SVM) with the EN-ICSA feature selection method, performs exceptionally well compared to other traditional machine learning and feature selection methods, achieving an accuracy of 91.94%, precision of 92.32%, recall of 89.85%, and F1-score of 90.82%. |
| format | Article |
| id | doaj-art-b3e512a143b6483ebb4ddf5fc27d11d9 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-b3e512a143b6483ebb4ddf5fc27d11d92025-08-20T03:17:08ZengPeerJ Inc.PeerJ Computer Science2376-59922025-07-0111e300010.7717/peerj-cs.3000Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithmLing Li0Tao Yue1Hui Wu2Yanping Zhao3Qinmei Liu4Hairong Zhang5Wei Xu6College of Communication Engineering, Jilin University, Changchun, Jilin, ChinaCollege of Communication Engineering, Jilin University, Changchun, Jilin, ChinaDepartment of Neonatology, The First Hospital of Jilin University, Changchun, Jilin, ChinaCollege of Communication Engineering, Jilin University, Changchun, Jilin, ChinaDepartment of Neonatology, The First Hospital of Jilin University, Changchun, Jilin, ChinaCollege of Communication Engineering, Jilin University, Changchun, Jilin, ChinaDepartment of Neonatology, The First Hospital of Jilin University, Changchun, Jilin, ChinaNeonatal brain injury carries the risk of neurological sequelae such as epileptic seizures, cerebral palsy, intellectual disability, and even death. Classification methods based on electroencephalography (EEG) signals and machine learning algorithms are crucial for assessing neonatal brain injury. However, classification methods that utilise all features from the original EEG signals may result in lengthy training and classification times, thereby reducing the performance of the classification system. This article presents a novel classification system with a proposed feature selection method for assessing neonatal brain injury, in which the feature selection method is combined using elastic net (EN) regression and an improved crow search algorithm (ICSA), named EN-ICSA. In the EN-ICSA method, EN regression is used to conduct the pre-screening of features. The ICSA is utilised to select the essential figures further by introducing the dynamic perception probability for deciding whether to search locally or globally, a novel neighbor-following strategy for the local search and a global search strategy according to the crow’s search experience, resulting in accelerating the search efficiency while effectively avoiding falling into local optima. Experimental results demonstrate that the proposed system, based on support vector machine (SVM) with the EN-ICSA feature selection method, performs exceptionally well compared to other traditional machine learning and feature selection methods, achieving an accuracy of 91.94%, precision of 92.32%, recall of 89.85%, and F1-score of 90.82%.https://peerj.com/articles/cs-3000.pdfNeonatal brain injuryEEGFeature selectionMachine learning |
| spellingShingle | Ling Li Tao Yue Hui Wu Yanping Zhao Qinmei Liu Hairong Zhang Wei Xu Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm PeerJ Computer Science Neonatal brain injury EEG Feature selection Machine learning |
| title | Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm |
| title_full | Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm |
| title_fullStr | Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm |
| title_full_unstemmed | Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm |
| title_short | Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm |
| title_sort | effective classification for neonatal brain injury using eeg feature selection based on elastic net regression and improved crow search algorithm |
| topic | Neonatal brain injury EEG Feature selection Machine learning |
| url | https://peerj.com/articles/cs-3000.pdf |
| work_keys_str_mv | AT lingli effectiveclassificationforneonatalbraininjuryusingeegfeatureselectionbasedonelasticnetregressionandimprovedcrowsearchalgorithm AT taoyue effectiveclassificationforneonatalbraininjuryusingeegfeatureselectionbasedonelasticnetregressionandimprovedcrowsearchalgorithm AT huiwu effectiveclassificationforneonatalbraininjuryusingeegfeatureselectionbasedonelasticnetregressionandimprovedcrowsearchalgorithm AT yanpingzhao effectiveclassificationforneonatalbraininjuryusingeegfeatureselectionbasedonelasticnetregressionandimprovedcrowsearchalgorithm AT qinmeiliu effectiveclassificationforneonatalbraininjuryusingeegfeatureselectionbasedonelasticnetregressionandimprovedcrowsearchalgorithm AT hairongzhang effectiveclassificationforneonatalbraininjuryusingeegfeatureselectionbasedonelasticnetregressionandimprovedcrowsearchalgorithm AT weixu effectiveclassificationforneonatalbraininjuryusingeegfeatureselectionbasedonelasticnetregressionandimprovedcrowsearchalgorithm |