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Epileptic Classification With Deep-Transfer-Learning-Based Feature Fusion Algorithm
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DOI：10.1109/TCDS.2021.3064228.png)
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摘要
En 中文
Epilepsy ictal detection based on scalp electroencephalograms (EEGs) has been comprehensively studied in the past decades. But few attentions have been paid to the preictal classification. In this article, a comprehensive study on epileptic state classification based on deep transfer learning (TL) is presented. The main contributions include: 1) the subband mean amplitude spectrum (MAS) map that characterizes the typical rhythms of brain activities is extracted for EEG representation; 2) five representative deep neural networks (DNNs) pretrained on ImageNet are applied for EEG feature TL; and 3) a 7-layer hierarchical neural network (HNN) that consists of three fully connected (Fc) and three dropout layers followed by a Softmax layer is developed to perform the epileptic state probability learning and classification. Experiments on the benchmark CHB-MIT and iNeuro EEG databases that contain several different types of seizures show that the proposed algorithm achieves the highest overall accuracies of 96.97% and 87.87% on the 5-state epileptic classification, respectively, that outperforms many existing state-of-the-art methods presented in this article.
Keyword:
Feature extraction
Electroencephalography
Databases
Brain modeling
Epilepsy
Transfer learning
Training
Deep transfer learning
deep neural networks (DNNs)
mean amplitude spectrum (MAS)
preictal classification
seizure detection
期刊
IF:
4.9
论文数:
1.0K
被引数:
3.5K
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