Seizure Type Detection Using EEG Signals Based on Phase Synchronization and Deep Learning

Epileptic seizure occurs due to the intricate reorganization of neural networks in the brain that can be identified by using Electroencephalogram (EEG) signals. Several attempts have been made at its automatic detection by involving several machine learning algorithms, but fewer efforts have been made at the discrimination of its types. Eventually, accurate identification of different types of seizures can play an important role in clinical care, diagnosis, and preference for propitious drugs. However, its discrimination is very challenging due to indiscernible variation and distinct preeminent synchronization among them. Meanwhile, deep learning (DL), that automatically identifies feature vectors from input, has shown notable performance in image classification and could be suitable. However, its effective performance relies on how the 2D images are generated from 1D EEG followed by its feeding in the DL pipeline. Certainly, during a seizure, significant changes in phase synchronization among EEG channels can be observed, which can be exploited in the discrimination of seizure types. Therefore, in this work, 2D images were generated based on a phase synchronization matrix by measuring mean phase coherence among each pair of common EEG channels and fed into a convolution neural network (CNN) to classify three seizure types (absence, complex partial, and myoclonic seizures). For validation, an EEG dataset from the Temple University Hospital was used. The classification performance was evaluated in terms of accuracy, sensitivity, specificity, and weighted F1-score which reached up to 83.30%, 91.43%, 82.90%, and 83.03% respectively, which is significantly high. Further, through a 5-fold cross-validation, the proposed method shows its robustness.