Deep Learning Approach for Epileptic Focus Localization

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 t...

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Vydané v:IEEE transactions on biomedical circuits and systems Ročník 14; číslo 2; s. 209 - 220
Hlavní autori: Daoud, Hisham, Bayoumi, Magdy
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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.
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SourceType-Scholarly Journals-1
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ISSN:1932-4545
1940-9990
1940-9990
DOI:10.1109/TBCAS.2019.2957087