Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals
Article
Article Title | Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals |
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ERA Journal ID | 650 |
Article Category | Article |
Authors | Diykh, Mohammed (Author), Miften, Firas Sabar (Author), Abdulla, Shahab (Author), Deo, Ravinesh C. (Author), Siuly, Siuly (Author), Green, Jonathan H. (Author) and Oudah, Atheer Y. (Author) |
Journal Title | Measurement |
Journal Citation | 190 (110731), pp. 1-13 |
Number of Pages | 13 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0263-2241 |
1873-412X | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.measurement.2022.110731 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0263224122000355 |
Abstract | Seizure detection is a particularly difficult task for neurologists to correctly identify the Electroencephalography (EEG)-based neonatal seizures in a visual manner. There is a strong demand to recognize the seizures in more automatic manner. Developing an expert seizure detection system with an acceptable performance level can partly fill this research gap. This paper proposes a new framework for the automated detection of neonatal seizures based on the Morse Wavelet approach that is coupled with a local binary pattern algorithm, and a graph-based community detection algorithm. An ensemble classifier method is designed to detect neonatal seizures prevalent in EEG signals. Our findings show that only 59 of the texture features can exhibit the abnormal increase in an EEG amplitude and the spikes notable during a seizure. The present results demonstrate that the proposed seizure detection model is more accurate for the detection of seizures compared with some of the traditional approaches. |
Keywords | Electroencephalogram (EEG) Neonatal seizure detection Morse wavelet Local binary pattern |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 329999. Other biomedical and clinical sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Southern Queensland |
University of Thi-Qar, Iraq | |
USQ College | |
School of Mathematics, Physics and Computing | |
Victoria University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7100/texture-analysis-based-graph-approach-for-automatic-detection-of-neonatal-seizure-from-multi-channel-eeg-signals
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