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Li M, Li J, Song Z, Deng H, Xu J, Xu G, Liao W. EEGNet-based multi-source domain filter for BCI transfer learning. Med Biol Eng Comput 2024; 62:675-686. [PMID: 37982955 DOI: 10.1007/s11517-023-02967-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/06/2023] [Indexed: 11/21/2023]
Abstract
Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost.
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Affiliation(s)
- Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China.
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China.
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China.
| | - Jundi Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Zhiyong Song
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Jiaming Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Wenzhe Liao
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
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2
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Jibon FA, Jamil Chowdhury AR, Miraz MH, Jin HH, Khandaker MU, Sultana S, Nur S, Siddiqui FH, Kamal AHM, Salman M, Youssef AAF. Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal. Digit Health 2024; 10:20552076241249874. [PMID: 38726217 PMCID: PMC11080778 DOI: 10.1177/20552076241249874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2024] [Indexed: 05/12/2024] Open
Abstract
Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.
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Affiliation(s)
- Ferdaus Anam Jibon
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - A. R. Jamil Chowdhury
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Mahadi Hasan Miraz
- Department of Management, Marketing and Digital Business, Faculty of Business, Curtin University Malaysia, Miri, Malaysia
| | - Hwang Ha Jin
- Department of Business Analytics, Sunway University, Bandar Sunway, Selangor, Malaysia
| | - Mayeen Uddin Khandaker
- Applied Physics and Radiation Technologies Group, CCDCU, School of Engineering and Technology, Sunway University, Bandar Sunway, Selangor, Malaysia
- Faculty of Graduate Studies, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka, Bangladesh
| | - Sajia Sultana
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Sifat Nur
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Fazlul Hasan Siddiqui
- Department of Computer Science & Engineering, Dhaka
University of Engineering & Technology (DUET), Gazipur, Dhaka, Bangladesh
| | - AHM Kamal
- Department of Computer Science & Engineering, Jatiya Kabi Kazi Nazrul Islam University (JKKNIU), Trishal, Mymensingh, Bangladesh
| | - Mohammad Salman
- College of Engineering and Technology, American University of the Middle East, Kuwait
| | - Ahmed A. F. Youssef
- College of Engineering and Technology, American University of the Middle East, Kuwait
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3
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Zhang Y, Li X, Wang S, Shen H, Huang K. A robust seizure detection and prediction method with feature selection and spatio-temporal casual neural network model. J Neural Eng 2023; 20:056036. [PMID: 37793368 DOI: 10.1088/1741-2552/acfff5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 10/04/2023] [Indexed: 10/06/2023]
Abstract
Objective.Epilepsy is a fairly common condition that affects the brain and causes frequent seizures. The sudden and recurring epilepsy brings a series of safety hazards to patients, which seriously affects the quality of their life. Therefore, real-time diagnosis of electroencephalogram (EEG) in epilepsy patients is of great significance. However, the conventional methods take in a tremendous amount of features to train the models, resulting in high computation cost and low portability. Our objective is to propose an efficient, light and robust seizure detecting and predicting algorithm.Approach.The algorithm is based on an interpretative feature selection method and spatial-temporal causal neural network (STCNN). The feature selection method eliminates the interference factors between different features and reduces the model size and training difficulties. The STCNN model takes both temporal and spatial information to accurately and dynamically track and diagnose the changing of the features. Considering the differences between medical application scenarios and patients, leave-one-out cross validation (LOOCV) and cross-patient validation (CPV) methods are used to conduct experiments on the dataset collected at the Children's Hospital Boston (CHB-MIT), Siena and Kaggle competition datasets.Main results.In LOOCV-based method, the detection accuracy and prediction sensitivity have been improved. A significant improvement is also achieved in the CPV-based method.Significance.The experimental results show that our proposed algorithm exhibits superior performance and robustness in seizure detection and prediction, which indicates it has higher capability to deal with different and complicated clinical situations.
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Affiliation(s)
- Yuanming Zhang
- Zhejiang University, 38 Zheda Road, Hangzhou, People's Republic of China
| | - Xin Li
- Zhejiang University, 38 Zheda Road, Hangzhou, People's Republic of China
| | - Shuang Wang
- Zhejiang University, 38 Zheda Road, Hangzhou, People's Republic of China
| | - Haibin Shen
- Zhejiang University, 38 Zheda Road, Hangzhou, People's Republic of China
| | - Kejie Huang
- Zhejiang University, 38 Zheda Road, Hangzhou, People's Republic of China
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Pandey A, Singh SK, Udmale SS, Shukla K. An intelligent optimized deep learning model to achieve early prediction of epileptic seizures. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Lopes F, Leal A, Pinto MF, Dourado A, Schulze-Bonhage A, Dümpelmann M, Teixeira C. Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models. Sci Rep 2023; 13:5918. [PMID: 37041158 PMCID: PMC10090199 DOI: 10.1038/s41598-023-30864-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/02/2023] [Indexed: 04/13/2023] Open
Abstract
The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models.
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Affiliation(s)
- Fábio Lopes
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
- Epilepsy Center, Department Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Adriana Leal
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Mauro F Pinto
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - António Dourado
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - César Teixeira
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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Qi N, Piao Y, Yu P, Tan B. Predicting epileptic seizures based on EEG signals using spatial depth features of a 3D-2D hybrid CNN. Med Biol Eng Comput 2023:10.1007/s11517-023-02792-4. [PMID: 36952120 DOI: 10.1007/s11517-023-02792-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/24/2023] [Indexed: 03/24/2023]
Abstract
Epilepsy is a recurrent chronic brain disease that affects nearly 75 million people around the world. Therefore, the ability to reliably predict epileptic seizures would be instrumental for implementing interventions to reduce brain injury and improve patients' quality of life. In addition to classical machine learning algorithms and feature engineering methods, the use of electroencephalography (EEG) to predict seizures has gradually become a mainstream trend. Here, we propose a patient-specific method to predict epileptic seizures based on EEG data acquired using spatial depth features of a three-dimensional-two-dimensional hybrid convolutional neural network (3D-2D HyCNN) model. This method facilitates the acquisition of abundant and reliable deep features from multi-channel EEG signals. We first developed a reliable data preprocessing method to reconstruct time-series EEG signals into 3D feature images. Then, the 3D-2D HyCNN model was used to extract correlation features between multiple channels of EEG signals, which are automatically exploited by the network to improve seizure prediction. The method achieved accuracy of 98.43% and 93.11%, sensitivity of 98.58% and 90.98%, and specificity of 96.86% and 92.39% on the CHB-MIT Scalp EEG dataset and the American Epilepsy Society Epilepsy Prediction Challenge dataset, respectively. The results revealed that the new algorithm is reliable. Graphical Abstract A new patient-specific epilepsy prediction approach.
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Affiliation(s)
- Nan Qi
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Yan Piao
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China.
| | - Peng Yu
- Department of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Baolin Tan
- Shenzhen Yinglun Technology Co. LTD., Shenzhen, China
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Zhang T, Chen W, Chen X. Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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Assali I, Jlassi I, Aissi M, Blaiech AG, Carrère M, Bedoui MH. Comparison by multivariate auto-regressive method of seizure prediction for real patients and virtual patients. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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