Name
Title | ||
|---|---|---|
Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals. |
Abstract | ||
|---|---|---|
Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80& x0025;. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2976156 | IEEE ACCESS |
Keywords | DocType | Volume |
Electroencephalography,Machine learning,Epilepsy,Feature extraction,Biological neural networks,Brain modeling,Tumors,Deep learning,C-LSTM,epileptic seizure,high-dimension electroencephalogram (EEG) | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 2 | 0.36 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Liu | 1 | 2 | 0.70 |
| Yu-Xuan Huang | 2 | 2 | 0.36 |
| Xuexi Zhang | 3 | 6 | 2.78 |
| Wen Qi | 4 | 14 | 2.40 |
| Jing Guo | 5 | 26 | 2.87 |
| Yingbai Hu | 6 | 56 | 10.78 |
| Longbin Zhang | 7 | 6 | 2.18 |
| Hang Su | 8 | 8 | 1.14 |