Deep Learning Approach for Epileptic Focus Localization

• Published in: IEEE transactions on biomedical circuits and systems Vol. 14; no. 2; pp. 209 - 220
• Main Authors: Daoud, Hisham, Bayoumi, Magdy
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain modeling; Classification; Clustering; Coders; Convolution; convolutional autoen-coder; Decision making; Deep Learning; EEG; Electroencephalography; Electroencephalography - methods; Epilepsy; Epilepsy - diagnosis; epileptic focus localization; Feature extraction; Humans; Localization; Machine learning; Multilayers; Seizures; Seizures - diagnosis; Signal Processing, Computer-Assisted; Surgery; Temporal variations; Training; Unsupervised Machine Learning; variational autoencoder
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2019.2957087

The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from uncontrollable seizures. This surgery usually aims to remove the epileptogenic region which requires precise characterization of that area using the EEG recordings. In this paper, we propose two methods based on deep learning targeting accurate automatic epileptic focus localization using the non-stationary EEG recordings. Our first proposed method is based on semi-supervised learning, in which a deep convolutional autoencoder is trained and then the pre-trained encoder is used with multi-layer perceptron as a classifier. The goal is to determine the location of the EEG signal that is responsible for the epileptic activity. In the second proposed method, unsupervised learning scheme is implemented by merging deep convolutional variational autoencoder and K-means algorithm for clustering the iEEG signals into two distinct clusters based on the seizure source. The proposed methods automate and integrate the features extraction and classification processes instead of manually extracting the features as done in the previous studies. Dimensionality reduction is achieved using the autoencoder, while the important spatio-temporal features are extracted from the EEG recordings using the convolutional layers. Moreover, we implemented the inference network of the semi-supervised model on FPGA. The results of our experiments demonstrate high classification accuracy and clustering performance in localizing the epileptic focus compared with the state of the art.