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Article

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

1
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
2
Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
3
Robotics Institute, School of Mechanical Engineering & Automation, BeiHang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
4
Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi 214001, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Dimiter Prodanov, Newton Howard and Jose Lujan
Brain Sci. 2021, 11(5), 615; https://doi.org/10.3390/brainsci11050615
Received: 8 April 2021 / Revised: 4 May 2021 / Accepted: 7 May 2021 / Published: 11 May 2021
(This article belongs to the Special Issue Neuroinformatics and Signal Processing)
Surgical intervention or the control of drug-refractory epilepsy requires accurate analysis of invasive inspection intracranial EEG (iEEG) data. A multi-branch deep learning fusion model is proposed to identify epileptogenic signals from the epileptogenic area of the brain. The classical approach extracts multi-domain signal wave features to construct a time-series feature sequence and then abstracts it through the bi-directional long short-term memory attention machine (Bi-LSTM-AM) classifier. The deep learning approach uses raw time-series signals to build a one-dimensional convolutional neural network (1D-CNN) to achieve end-to-end deep feature extraction and signal detection. These two branches are integrated to obtain deep fusion features and results. Resampling is employed to split the imbalanced epileptogenic and non-epileptogenic samples into balanced subsets for clinical validation. The model is validated over two publicly available benchmark iEEG databases to verify its effectiveness on a private, large-scale, clinical stereo EEG database. The model achieves high sensitivity (97.78%), accuracy (97.60%), and specificity (97.42%) on the Bern–Barcelona database, surpassing the performance of existing state-of-the-art techniques. It is then demonstrated on a clinical dataset with an average intra-subject accuracy of 92.53% and cross-subject accuracy of 88.03%. The results suggest that the proposed method is a valuable and extremely robust approach to help researchers and clinicians develop an automated method to identify the source of iEEG signals. View Full-Text
Keywords: intracranial EEG (iEEG); SEEG; epileptogenic signals identification; multi-branch deep learning fusion intracranial EEG (iEEG); SEEG; epileptogenic signals identification; multi-branch deep learning fusion
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MDPI and ACS Style

Wang, Y.; Dai, Y.; Liu, Z.; Guo, J.; Cao, G.; Ouyang, M.; Liu, D.; Shan, Y.; Kang, G.; Zhao, G. Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation. Brain Sci. 2021, 11, 615. https://doi.org/10.3390/brainsci11050615

AMA Style

Wang Y, Dai Y, Liu Z, Guo J, Cao G, Ouyang M, Liu D, Shan Y, Kang G, Zhao G. Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation. Brain Sciences. 2021; 11(5):615. https://doi.org/10.3390/brainsci11050615

Chicago/Turabian Style

Wang, Yiping, Yang Dai, Zimo Liu, Jinjie Guo, Gongpeng Cao, Mowei Ouyang, Da Liu, Yongzhi Shan, Guixia Kang, and Guoguang Zhao. 2021. "Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation" Brain Sciences 11, no. 5: 615. https://doi.org/10.3390/brainsci11050615

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