1. Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation.
- Author
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Wang, Yiping, Dai, Yang, Liu, Zimo, Guo, Jinjie, Cao, Gongpeng, Ouyang, Mowei, Liu, Da, Shan, Yongzhi, Kang, Guixia, Zhao, Guoguang, Prodanov, Dimiter, Howard, Newton, and Lujan, Jose
- Subjects
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DEEP learning , *SIGNAL convolution , *MODEL validation , *ELECTROENCEPHALOGRAPHY , *CONVOLUTIONAL neural networks , *MEDICAL personnel - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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