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

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

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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