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| Epileptic States Recognition using Transfer Learning and Dilated CNN |
| Shen Lei1,2,3,4, Geng Xinyi1,2,3,4, Wang Shouyan1,2,3,4#* |
1(Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China)
2(Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China)
3(Shanghai Engineering Research Center of AI & Robotics, Fudan University, Shanghai 200433, China)
4(Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai 200433, China) |
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Abstract The automatic detection and seizure diagnosis of EEG signals in patients with epilepsy is of great significance for clinical treatment of epilepsy. Aiming at solving the problem in the conventional method that the labeled training data volume is insufficient and the classification accuracy of seizure is low due to the inconsistent distribution of training and test data, a joint knowledge transfer method between domains was proposed in this work. Firstly, the EEG signal was decomposed by four-layer wavelet packet, and the wavelet packet decomposition coefficients of 16 frequency bands were extracted as features. The marginal and joint distribution iterative adaptation were used to complete the knowledge transfer between the source and target domain. The dilated convolutional neural network was trained to complete the target domain recognition. In this study, the algorithms were estimated on two public EEG datasets including CHB-MIT dataset (22 patients, 790 hours' recording) and Bonn dataset (5 groups, one hundred 23.6 s episodes in each group). The experimental results showed that the average recognition accuracy, sensitivity and specificity of the proposed method for different epilepsy states was 96.8%,96.1%,96.4% on CHB-MIT dataset respectively. The average recognition accuracy was 96.9% on the Boon dataset, which effectively improved the comprehensive performance of seizure detection and achieve reliable detection results.
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Received: 05 December 2019
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