Fractal dimension undirected correlation graph-based support vector machine model for identification of focal and non-focal electroencephalography signals

Diykh, Mohammed and Abdulla, Shahab and Saleh, Khalid and Deo, Ravinesh C. (2019) Fractal dimension undirected correlation graph-based support vector machine model for identification of focal and non-focal electroencephalography signals. Biomedical Signal Processing and Control, 54 (Article 101611). pp. 1-10. ISSN 1746-8094

Abstract

Recognition of focal (FC) and non-focal (NFC) Electroencephalography (EEG) signals is crucial for clinical diagnosis used to localise and aid in medical treatment of the affected region in the human brain. Developing an artificial intelligence system that can adequately identify these affected regions can support the clinical diagnosis of brain disease. In this study, we develop a new model called a fractal dimension (FD) of the undirected graph (NG) based on a sine cosine driven support vector machine (FD-NG model utilising the SCA-SVM) algorithm for identifying the focal and non-focal EEG signals. Each EEG signal is partitioned into its respective segments and each segment is divided into clusters using a sliding window technique. To reduce the dimensionality of each cluster, a set of best features is extracted. Three types of input features are considered: linear features (LF), statistical features (SF), and features based on time domain (TD). These are investigated and extracted from each cluster. As a result, each EEG signal is represented by a series of reduced segments and is then forwarded to the proposed FD-NG based SCA-SVM model. The model considers each segment as a node and a link is built between each pair of nodes based on their degree of similarity. The FD of graphs are used as inputs to the SCA-SVM model to classify the EEG signal into FC and NFC components. The obtained results, which also demonstrates the practicality of the approach, confirm that the proposed model surpasses the performance of existing state-of- the-art techniques.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Open Access College (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Open Access College (1 Jul 2013 -)
Date Deposited: 09 Aug 2019 03:12
Last Modified: 25 Sep 2019 05:36
Uncontrolled Keywords: fractal dimension; correlation graphs; focal EEG signals; non-focal EEG signals; SCA-SVM
Fields of Research : 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1016/j.bspc.2019.101611
URI: http://eprints.usq.edu.au/id/eprint/36864

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