Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals

Diykh, Mohammed and Miften, Firas Sabar and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Siuly, Siuly and Green, Jonathan H. ORCID: https://orcid.org/0000-0003-1468-1970 and Oudah, Atheer Y. (2022) Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals. Measurement, 190 (110731). pp. 1-13. ISSN 0263-2241


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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - USQ College (8 Jun 2020 -)
Faculty/School / Institute/Centre: Current - USQ College (8 Jun 2020 -)
Date Deposited: 04 Mar 2022 04:40
Last Modified: 11 Mar 2022 05:56
Uncontrolled Keywords: Electroencephalogram (EEG) Neonatal seizure detection Morse wavelet Local binary pattern
Fields of Research (2008): 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
Fields of Research (2020): 32 BIOMEDICAL AND CLINICAL SCIENCES > 3299 Other biomedical and clinical sciences > 329999 Other biomedical and clinical sciences not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Socio-Economic Objectives (2020): 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified
Identification Number or DOI: https://doi.org/10.1016/j.measurement.2022.110731
URI: http://eprints.usq.edu.au/id/eprint/46953

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