The use of transformation techniques (such as a wavelet transform, Fourier
transform, or hybrid transform) to detect epileptic seizures by means of EEG signals is
not adequate because these signals have a nonstationary and nonlinear nature. This
paper reports on the design of a novel technique based, instead, on the domain of
graphs. The dimensionality of each single EEG channel is reduced using a
segmentation technique, and each EEG channel is then mapped onto an undirected
weighted graph. A set of structural and topological graph characteristics is extracted
and investigated, and several machine learning techniques are utilized to categorize the
graph’s attributes. The results demonstrate that the use of graphs improves the quality
of epileptic seizure detection. The proposed method can identify EEG abnormities that
are difficult to detect accurately using other transformation techniques, especially when
dealing with EEG big data.
Keywords: Epileptic EEG Signals, Graphs, Modularity, Multi-Channel,
Statistical Features.
