Affiliations: [a] University of Central Florida, Orlando, FL, USA | [b] AdventHealth Hospital, Orlando, FL, USA | [c] AdventHealth, Orlando, FL, USA
Correspondence:
[*]
Corresponding author: Elakkat D. Gireesh, University of Central Florida, Orlando, USA. E-mail: elakkat@knights.ucf.edu.
Abstract: Deep Neural Networks (DNN) have significantly improved the capabilities for analysis and classification of data, including that of biomedical signals (eg. ElectroencephalogramEEG). Optimal classification of EEG signals from seizure onset zones has been challenging especially given the complexity of signals arising from multiple locations. Also, underlying electrophysiological abnormalities which signify epileptogenic zones have not been clearly defined. Previous studies have demonstrated, automatic feature generation based on deep learning as a useful tool for interictal epileptiform discharge (IEDs) detection. Also signals with transformations have been used in convolutional neural network (CNN) based models in the past for classifying EEG data. We explored the use of deep learning for identification of the seizure onset zones using regular dense neural network and CNN based models. After the training the model using sample data the results were validated with a smaller percentage (10%) of the data. The models were noted to be accurate in predicting the seizure onset zones with significant degree of accuracy (87–99%) with a much shorter duration of signal recorded, compared to previous studies. We further investigated model’s decision-making process with heatmapping (gradient-weighted class activation map: Grad-CAM) approach, combining with signal processing using Hilbert transform. To identify the visible features in the signal, which maximally contribute to the seizure onset zone prediction, correlation of heatmap and analytical signal of the EEG, was calculated. A high correlation between heatmap and analytical signal was noted, suggesting that the model may be utilizing the higher power regions of the data in decision making process. This study demonstrates the potential use of DNN based strategies in identifying the epileptogenic zones in the intracranial EEG. It also shows the heatmapping strategies can help in establishing how specific signal patterns may be contributing to the decision making of DNN.
Keywords: Deep neural network, heatmap, Grad-CAM, artificial intelligence, iEEG, epilepsy
