Transfer Learning for the Identification of Paediatric EEGs With Interictal Epileptiform Abnormalities

EEG is a test that helps in the clinical diagnosis of epilepsy. Epilepsy diagnosis is facilitated by establishing the presence of interictal epileptiform abnormalities on EEG, which predict an increased risk of seizure. The identification of interictal epileptiform discharges is a time-consuming task that requires highly-trained experts. A method to assist in the recognition of EEGs with epileptiform abnormalities was developed using transfer learning on multiple channels of paediatric EEGs, without the use of human annotations. The dataset included 350 children with normal EEGs and 597 children with interictal abnormalities, and it was divided into training data (n=452), validation data (n=112), and testing data (n=383). Spectrograms from each EEG signal channel were used as input for five pre-trained transfer learning models (Inception, ResNet, DenseNet, VGG16 and VGG19) and traditional feature-based machine learning methods were developed as a benchmark. A comparison was made between a transfer learning-based method and a traditional feature-based machine learning algorithm. The results revealed that the transfer learning-based method outperformed the feature-based machine learning methods, achieving an accuracy of 77%, an F1 score of 0.85, and a balanced accuracy of 77% on the test set. Our transfer learning-based method can identify interictal abnormalities without the need for feature estimation by domain experts or human annotations. This method can assist in the recognition of EEGs with epileptiform abnormalities in children thereby facilitating the clinical diagnosis of epilepsy.