Interictal Epileptiform Discharge Detection Using Time-Frequency Analysis and Transfer Learning

Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automated detection of IEDs is crucial for assisting clinicians in epilepsy diagnosis as they can help identify the extent of cortical irritations and may indicate an upcoming seizure, thus helping in preventing seizure. It also minimizes visual inspection of very long EEG signals by physicians. This paper presents a transfer-learning-based approach for analyzing time-frequency representations of different types of IEDs from scalp EEG data using a fine-tuned deep residual network. The proposed method was evaluated using the publicly available Temple University Events EEG dataset. Experimental results show that our method demonstrates promising performance, by achieving an F1-score of 88.52% on this dataset for binary classification of IEDs.