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Chung YG, Cho A, Kim H, Kim KJ. Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset. Front Neurol 2024; 15:1389731. [PMID: 38836000 PMCID: PMC11148866 DOI: 10.3389/fneur.2024.1389731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. Methods We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Results Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. Discussion We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.
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Affiliation(s)
- Yoon Gi Chung
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Anna Cho
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki Joong Kim
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
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Ru Y, Wei Z, An G, Chen H. Combining data augmentation and deep learning for improved epilepsy detection. Front Neurol 2024; 15:1378076. [PMID: 38633533 PMCID: PMC11021591 DOI: 10.3389/fneur.2024.1378076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction In recent years, the use of EEG signals for seizure detection has gained widespread academic attention. Aiming at the problem of overfitting deep learning models due to the small number of EEG signal data during epilepsy detection, this paper proposes an epilepsy detection method that combines data augmentation and deep learning. Methods First, the Adversarial and Mixup Data Augmentation (AMDA) method is used to realize the data augmentation, which effectively enriches the number of training samples. To further improve the classification accuracy and robustness of epilepsy detection, this paper proposes a one-dimensional convolutional neural network and gated recurrent unit (AM-1D CNN-GRU) network model based on attention mechanism for epilepsy detection. Results and discussion The experimental results show that the performance of epilepsy detection achieved by using augmented data is significantly improved, and the accuracy, sensitivity, and area under the subject's working characteristic curve are up to 96.06, 95.48%, and 0.9637, respectively. Compared with the non-augmented data, all indicators are increased by more than 6.2%. Meanwhile, the detection performance was significantly improved compared with other epilepsy detection methods. The results of this research can provide a reference for the clinical application of epilepsy detection.
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Affiliation(s)
- Yandong Ru
- School of Information Engineering, Zhejiang Ocean University, Zhoushan, China
- Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, China
| | - Zheng Wei
- School of Electronics and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
| | - Gaoyang An
- School of Electronics and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
| | - Hongming Chen
- School of Information Engineering, Zhejiang Ocean University, Zhoushan, China
- Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, China
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3
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Wu X, Zhang D, Li G, Gao X, Metcalfe B, Chen L. Data augmentation for invasive brain-computer interfaces based on stereo-electroencephalography (SEEG). J Neural Eng 2024; 21:016026. [PMID: 38237174 DOI: 10.1088/1741-2552/ad200e] [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: 06/06/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024]
Abstract
Objective.Deep learning is increasingly used for brain-computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness. However, all the existing studies on brain signals were based on convolutional neural networks and ignored the temporal dependence. This paper attempted to enhance generative models by capturing the temporal relationship from a time-series perspective.Approach. A conditional generative network (conditional transformer-based generative adversarial network (cTGAN)) based on the transformer model was proposed. The proposed method was tested using a stereo-electroencephalography (SEEG) dataset which was recorded from eight epileptic patients performing five different movements. Three other commonly used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE), and conditional Wasserstein generative adversarial network with gradient penalty (cWGANGP). Using the proposed method, the artificial SEEG data was generated, and several metrics were used to compare the data quality, including visual inspection, cosine similarity (CS), Jensen-Shannon distance (JSD), and the effect on the performance of a deep learning-based classifier.Main results. Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE outputted inferior samples when visualized as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of CS and JSD and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively).Significance. This is the first time that DA methods have been applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.
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Affiliation(s)
- Xiaolong Wu
- The Centre for Autonomous Robotics (CENTAUR), Department of Electronic & Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Dingguo Zhang
- The Centre for Autonomous Robotics (CENTAUR), Department of Electronic & Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Guangye Li
- School of Mechanical Engineering, Shanghai Jiao Tong University, People's Republic of China
| | - Xin Gao
- The Centre for Autonomous Robotics (CENTAUR), Department of Electronic & Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Benjamin Metcalfe
- The Centre for Autonomous Robotics (CENTAUR), Department of Electronic & Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Liang Chen
- Liang Chen is with Huashan Hospital, Fudan University, People's Republic of China
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Zhang Z, Xiao M, Ji T, Jiang Y, Lin T, Zhou X, Lin Z. Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network. Front Neurosci 2024; 17:1303564. [PMID: 38268711 PMCID: PMC10805904 DOI: 10.3389/fnins.2023.1303564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024] Open
Abstract
Introduction Epilepsy is a global chronic disease that brings pain and inconvenience to patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that can be applied to any patient, an automatic cross-patient epilepsy seizure detection algorithm is of great significance. Spiking neural networks (SNNs) are modeled on biological neurons and are energy-efficient on neuromorphic hardware, which can be expected to better handle brain signals and benefit real-world, low-power applications. However, automatic epilepsy seizure detection rarely considers SNNs. Methods In this article, we have explored SNNs for cross-patient seizure detection and discovered that SNNs can achieve comparable state-of-the-art performance or a performance that is even better than artificial neural networks (ANNs). We propose an EEG-based spiking neural network (EESNN) with a recurrent spiking convolution structure, which may better take advantage of temporal and biological characteristics in EEG signals. Results We extensively evaluate the performance of different SNN structures, training methods, and time settings, which builds a solid basis for understanding and evaluation of SNNs in seizure detection. Moreover, we show that our EESNN model can achieve energy reduction by several orders of magnitude compared with ANNs according to the theoretical estimation. Discussion These results show the potential for building high-performance, low-power neuromorphic systems for seizure detection and also broaden real-world application scenarios of SNNs.
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Affiliation(s)
- Zongpeng Zhang
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Mingqing Xiao
- National Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China
| | - Taoyun Ji
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Yuwu Jiang
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Tong Lin
- National Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China
| | - Xiaohua Zhou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
- Peking University Chongqing Institute for Big Data, Chongqing, China
| | - Zhouchen Lin
- National Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China
- Institute for Artificial Intelligence, Peking University, Beijing, China
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Mahgoub A, Qaraqe M. Automatic detection of ictal activity in EEG using synchronization and chaos-based attributes. Med Biol Eng Comput 2023; 61:3387-3396. [PMID: 37673851 PMCID: PMC10746768 DOI: 10.1007/s11517-023-02916-w] [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: 12/21/2022] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
Automatic seizure onset detectors (SODs) have been proposed to alert epileptic patients when a seizure is about to happen and in turn improve their quality of life. Yet, the detectors proposed in literature are complex and difficult to implement in real-time as they utilize large feature sets with redundant and irrelevant features. Hence, the aim of this work is to propose a simple and lightweight SOD that exploits two characteristics that reflect the neuronal behavior during a seizure. Namely, the synchronization between EEG channels and the chaoticity of the EEG; synchronization was measured by the condition number while the recurrence period density entropy estimated the chaoticity of an EEG signal. A support vector machine was trained and tested on 10 patients from a scalp EEG dataset and was able to detect the considered seizures with a sensitivity of 100% and a false positives rate of 0.5 per hour. The results indicate that synchronization and chaos attributes can reflect the manifestation of seizures in EEG data and can be used to develop SODs. This work emphasizes that even a single relevant feature can produce an SOD with comparable performance to SODs that use many features.
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Affiliation(s)
- Asma Mahgoub
- Hamad Bin Khalifa University, Doha, Qatar.
- Qatar University, Doha, Qatar.
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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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Carrle FP, Hollenbenders Y, Reichenbach A. Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder. Front Neurosci 2023; 17:1219133. [PMID: 37849893 PMCID: PMC10577178 DOI: 10.3389/fnins.2023.1219133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/05/2023] [Indexed: 10/19/2023] Open
Abstract
Introduction Major depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising results. However, the generalizability of those models, a prerequisite for clinical application, is restricted by small datasets. One approach to train ML models with good generalizability is complementing the original with synthetic data produced by generative algorithms. Another advantage of synthetic data is the possibility of publishing the data for other researchers without risking patient data privacy. Synthetic EEG time-series have not yet been generated for two clinical populations like MDD patients and healthy controls. Methods We first reviewed 27 studies presenting EEG data augmentation with generative algorithms for classification tasks, like diagnosis, for the possibilities and shortcomings of recent methods. The subsequent empirical study generated EEG time-series based on two public datasets with 30/28 and 24/29 subjects (MDD/controls). To obtain baseline diagnostic accuracies, convolutional neural networks (CNN) were trained with time-series from each dataset. The data were synthesized with generative adversarial networks (GAN) consisting of CNNs. We evaluated the synthetic data qualitatively and quantitatively and finally used it for re-training the diagnostic model. Results The reviewed studies improved their classification accuracies by between 1 and 40% with the synthetic data. Our own diagnostic accuracy improved up to 10% for one dataset but not significantly for the other. We found a rich repertoire of generative models in the reviewed literature, solving various technical issues. A major shortcoming in the field is the lack of meaningful evaluation metrics for synthetic data. The few studies analyzing the data in the frequency domain, including our own, show that only some features can be produced truthfully. Discussion The systematic review combined with our own investigation provides an overview of the available methods for generating EEG data for a classification task, their possibilities, and shortcomings. The approach is promising and the technical basis is set. For a broad application of these techniques in neuroscience research or clinical application, the methods need fine-tuning facilitated by domain expertise in (clinical) EEG research.
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Affiliation(s)
- Friedrich Philipp Carrle
- Center for Machine Learning, Heilbronn University, Heilbronn, Germany
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Yasmin Hollenbenders
- Center for Machine Learning, Heilbronn University, Heilbronn, Germany
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Alexandra Reichenbach
- Center for Machine Learning, Heilbronn University, Heilbronn, Germany
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany
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Zheng S, Zhang X, Song P, Hu Y, Gong X, Peng X. Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages. Front Comput Neurosci 2023; 17:1211096. [PMID: 37841676 PMCID: PMC10570412 DOI: 10.3389/fncom.2023.1211096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/13/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky. Methods In this paper, five popular complexity indicators of EEG signal, including approximate entropy, sample entropy, permutation entropy, fuzzy entropy and Kolmogorov complexity, are computed from rat hippocampi to characterize the normal, acute, and chronic phases during epileptogenesis. Results of one-way ANOVA and principal component analysis both show that utilizing complexity features, we are able to easily identify differences between normal, acute, and chronic phases. We also propose an innovative framework for epilepsy detection based on graph convolutional neural network (GCNN) using multi-channel EEG complexity as input. Results Combining information of five complexity measures at eight channels, our GCNN model demonstrate superior ability in recognizing the normal, acute, and chronic phases. Experiments results show that our GCNN model reached the high prediction accuracy above 98% and F1 score above 97% among these three phases for each individual rat. Discussion Our research practice based on real data shows that EEG complexity characteristics are of great significance for recognizing different stages of epilepsy.
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Affiliation(s)
- Shiming Zheng
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
| | - Xiaopei Zhang
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
| | - Panpan Song
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xi Gong
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
| | - Xiaoling Peng
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
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Lih OS, Jahmunah V, Palmer EE, Barua PD, Dogan S, Tuncer T, García S, Molinari F, Acharya UR. EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population. Comput Biol Med 2023; 164:107312. [PMID: 37597408 DOI: 10.1016/j.compbiomed.2023.107312] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. METHOD The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. RESULTS Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. CONCLUSION The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.
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Affiliation(s)
- Oh Shu Lih
- Cogninet Australia, Sydney, NSW, 2010, Australia
| | - V Jahmunah
- School of Engineering, Nanyang Polytechnic, Singapore
| | - Elizabeth Emma Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, 2031, Australia; School of Women's and Children's Health, University of New South Wales, Randwick, 2031, Australia
| | - Prabal D Barua
- School of Business (Information System), University of Southern Queensland, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Salvador García
- Andalusian Institute of Data Science and Computational Intelligence, Department of Computer Science and Artificial Intelligence, University of Granada, Spain
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia.
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Tian Z, Hu B, Si Y, Wang Q. Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier. Brain Sci 2023; 13:brainsci13050820. [PMID: 37239292 DOI: 10.3390/brainsci13050820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 04/28/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.
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Affiliation(s)
- Ziwei Tian
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 101408, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Bingliang Hu
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Yang Si
- Department of Neurology, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Chengdu 610072, China
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
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Liu X, Ding X, Liu J, Nie W, Yuan Q. Automatic focal EEG identification based on deep reinforcement learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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12
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Safder SNUH, Akram MU, Dar MN, Khan AA, Khawaja SG, Subhani AR, Niazi IK, Gul S. Analysis of EEG signals using deep learning to highlight effects of vibration-based therapy on brain. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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13
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Habashi AG, Azab AM, Eldawlatly S, Aly GM. Generative adversarial networks in EEG analysis: an overview. J Neuroeng Rehabil 2023; 20:40. [PMID: 37038142 PMCID: PMC10088201 DOI: 10.1186/s12984-023-01169-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 03/30/2023] [Indexed: 04/12/2023] Open
Abstract
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.
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Affiliation(s)
- Ahmed G Habashi
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
| | - Ahmed M Azab
- Biomedical Engineering Department, Technical Research Center, Cairo, Egypt
| | - Seif Eldawlatly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt.
- Computer Science and Engineering Department, The American University in Cairo, Cairo, Egypt.
| | - Gamal M Aly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
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14
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Currey D, Craley J, Hsu D, Ahmed R, Venkataraman A. EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG. PLoS One 2023; 18:e0282268. [PMID: 36848345 PMCID: PMC9970073 DOI: 10.1371/journal.pone.0282268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 02/11/2023] [Indexed: 03/01/2023] Open
Abstract
Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.
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Affiliation(s)
- Danielle Currey
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeff Craley
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - David Hsu
- Department of Neurology, University of Wisconsin Madison, Madison, WI, United States of America
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin Madison, Madison, WI, United States of America
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States of America
- * E-mail:
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15
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Si X, Yang Z, Zhang X, Sun Y, Jin W, Wang L, Yin S, Ming D. Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN. J Neural Eng 2023; 20. [PMID: 36626831 DOI: 10.1088/1741-2552/acb1d9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 01/10/2023] [Indexed: 01/11/2023]
Abstract
Objective.Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics. However, they still have shortcomings of large memory footprints and slow inference speed.Approach.To solve the above problems of the current study, we propose a novel lightweight convolutional neural network model combining the Convolutional Block Attention Module (CBAM). Its performance for patient-independent seizure detection is evaluated on two long-term continuous iEEG datasets: SWEC-ETHZ and TJU-HH. Finally, we reproduce four other patient-independent methods to compare with our method and calculate the memory footprints and inference speed for all methods.Main results.Our method achieves 83.81% sensitivity (SEN) and 85.4% specificity (SPE) on the SWEC-ETHZ dataset and 86.63% SEN and 92.21% SPE on the TJU-HH dataset. In particular, it takes only 11 ms to infer 10 min iEEG (128 channels), and its memory footprint is only 22 kB. Compared to baseline methods, our method not only achieves better patient-independent seizure detection performance but also has a smaller memory footprint and faster inference speed.Significance.To our knowledge, this is the first iEEG-based patient-independent seizure detection study. This facilitates the application of seizure detection algorithms to the future clinic.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Zhuobin Yang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weipeng Jin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Le Wang
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Shaoya Yin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
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16
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Cimr D, Fujita H, Tomaskova H, Cimler R, Selamat A. Automatic seizure detection by convolutional neural networks with computational complexity analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107277. [PMID: 36463672 DOI: 10.1016/j.cmpb.2022.107277] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/20/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVES Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. METHODS The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. RESULTS Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. CONCLUSIONS Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.
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Affiliation(s)
- Dalibor Cimr
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam; DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain; Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia; Regional Research Center, Iwate Prefectural University, Iwate Japan.
| | - Hana Tomaskova
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Richard Cimler
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Ali Selamat
- Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
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17
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Jiang L, He J, Pan H, Wu D, Jiang T, Liu J. Seizure detection algorithm based on improved functional brain network structure feature extraction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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18
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Amiri M, Aghaeinia H, Amindavar HR. Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing transform. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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19
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Effects of Data Augmentation with the BNNSMOTE Algorithm in Seizure Detection Using 1D-MobileNet. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4114178. [PMID: 36578313 PMCID: PMC9792253 DOI: 10.1155/2022/4114178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/19/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022]
Abstract
Automatic seizure detection technology has important implications for reducing the workload of neurologists for epilepsy diagnosis and treatment. Due to the unpredictable nature of seizures, the imbalanced classification of seizure and nonseizure data continues to be challenging. In this work, we first propose a novel algorithm named the borderline nearest neighbor synthetic minority oversampling technique (BNNSMOTE) to address the imbalanced classification problem and improve seizure detection performance. The algorithm uses the nearest neighbor notion to generate nonseizure samples near the boundary, then determines the seizure samples that are difficult to learn at the boundary, and lastly selects seizure samples at random to be used in the synthesis of new samples. In view of the characteristic that electroencephalogram (EEG) signals are one-dimensional signals, we then develop a 1D-MobileNet model to validate the algorithm's performance. Results demonstrate that the proposed algorithm outperforms previous seizure detection methods on the CHB-MIT dataset, achieving an average accuracy of 99.40%, a recall value of 87.46%, a precision of 97.17%, and an F1-score of 91.90%, respectively. We also had considerable success when we used additional datasets for verification at the same time. Our algorithm's data augmentation effects are more pronounced and perform better at seizure detection than the existing imbalanced techniques. Besides, the model's parameters and calculation volume have been significantly reduced, making it more suitable for mobile terminals and embedded devices.
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20
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Peng R, Zhao C, Jiang J, Kuang G, Cui Y, Xu Y, Du H, Shao J, Wu D. TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2567-2576. [PMID: 36063519 DOI: 10.1109/tnsre.2022.3204540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized.
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21
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Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, Kong Y, Gorriz JM, Ramírez J, Khosravi A, Nahavandi S, Acharya UR. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 149:106053. [DOI: 10.1016/j.compbiomed.2022.106053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 02/01/2023]
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22
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N.J. S, M.S.P. S, S. TG. EEG-based classification of normal and seizure types using relaxed local neighbour difference pattern and artificial neural network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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23
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Seizure Prediction Based on Transformer Using Scalp Electroencephalogram. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094158] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Epilepsy is a chronic and recurrent brain dysfunction disease. An acute epileptic attack will interfere with a patient’s normal behavior and consciousness, having a great impact on their life. The purpose of this study was to design a seizure prediction model to improve the quality of patients’ lives and assist doctors in making diagnostic decisions. This paper presents a transformer-based seizure prediction model. Firstly, the time-frequency characteristics of electroencephalogram (EEG) signals were extracted by short-time Fourier transform (STFT). Secondly, a three transformer tower model was used to fuse and classify the features of the EEG signals. Finally, when combined with the attention mechanism of transformer networks, the EEG signal was processed as a whole, which solves the problem of length limitations in deep learning models. Experiments were conducted with a Children’s Hospital Boston and the Massachusetts Institute of Technology database to evaluate the performance of the model. The experimental results show that, compared with previous EEG classification models, our model can enhance the ability to use time, frequency, and channel information from EEG signals to improve the accuracy of seizure prediction.
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24
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Zhou J, Liu L, Leng Y, Yang Y, Gao B, Jiang Z, Nie W, Yuan Q. Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic. Int J Neural Syst 2022; 32:2250017. [DOI: 10.1142/s0129065722500174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.
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Affiliation(s)
- Jiazheng Zhou
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Li Liu
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yan Leng
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yuying Yang
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Bin Gao
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Zonghong Jiang
- College of Resources and Environment Engineering, Guizhou University, Guiyang 550025, P. R. China
| | - Weiwei Nie
- The First Affiliated Hospital of Shandong, First Medical University, Jinan 250014, P. R. China
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
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25
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Tuncer E, Doğru Bolat E. Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103462] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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26
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Craley J, Jouny C, Johnson E, Hsu D, Ahmed R, Venkataraman A. Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks. PLoS One 2022; 17:e0264537. [PMID: 35226686 PMCID: PMC8884583 DOI: 10.1371/journal.pone.0264537] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/13/2022] [Indexed: 12/02/2022] Open
Abstract
We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with recurrent neural networks to capture the evolution of seizure activity. Our unique training strategy aggregates individual electrode level predictions for patient-level seizure detection and localization. We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital. Our network achieves similar seizure detection performance to state-of-the-art methods and provides valuable localization information that has not previously been demonstrated in the literature. We also show the cross-site generalization capabilities of SZTrack on a dataset of 53 seizure recordings from 14 epilepsy patients acquired at the University of Wisconsin Madison. SZTrack is able to determine the lobe and hemisphere of origin in nearly all of these new patients without retraining the network. To our knowledge, SZTrack is the first end-to-end seizure tracking network using scalp EEG.
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Affiliation(s)
- Jeff Craley
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- * E-mail:
| | - Christophe Jouny
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Emily Johnson
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - David Hsu
- Department of Neurology, University of Wisconsin Madison, Madison, WI, United States of America
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin Madison, Madison, WI, United States of America
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
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27
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Ganti B, Chaitanya G, Balamurugan RS, Nagaraj N, Balasubramanian K, Pati S. Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG. Front Neurol 2022; 13:755094. [PMID: 35250803 PMCID: PMC8889931 DOI: 10.3389/fneur.2022.755094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Abstract
Seizure detection algorithms are often optimized to detect seizures from the epileptogenic cortex. However, in non-localizable epilepsies, the thalamus is frequently targeted for neuromodulation. Developing a reliable seizure detection algorithm from thalamic SEEG may facilitate the translation of closed-loop neuromodulation. Deep learning algorithms promise reliable seizure detectors, but the major impediment is the lack of larger samples of curated ictal thalamic SEEG needed for training classifiers. We aimed to investigate if synthetic data generated by temporal Generative Adversarial Networks (TGAN) can inflate the sample size to improve the performance of a deep learning classifier of ictal and interictal states from limited samples of thalamic SEEG. Thalamic SEEG from 13 patients (84 seizures) was obtained during stereo EEG evaluation for epilepsy surgery. Overall, TGAN generated synthetic data augmented the performance of the bidirectional Long-Short Term Memory (BiLSTM) performance in classifying thalamic ictal and baseline states. Adding synthetic data improved the accuracy of the detection model by 18.5%. Importantly, this approach can be applied to classify electrographic seizure onset patterns or develop patient-specific seizure detectors from implanted neuromodulation devices.
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Affiliation(s)
- Bhargava Ganti
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | - Ganne Chaitanya
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX, United States
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Nithin Nagaraj
- Consciousness Studies Programme, National Institute of Advanced Studies, Bengaluru, India
| | - Karthi Balasubramanian
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | - Sandipan Pati
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX, United States
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
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28
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Zhang Y, Yao S, Yang R, Liu X, Qiu W, Han L, Zhou W, Shang W. Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network. IEEE Trans Neural Syst Rehabil Eng 2022; 30:135-145. [PMID: 35030083 DOI: 10.1109/tnsre.2022.3143540] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG.
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29
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Cherian R, Kanaga EG. Theoretical and Methodological Analysis of EEG based Seizure Detection and Prediction: An Exhaustive Review. J Neurosci Methods 2022; 369:109483. [DOI: 10.1016/j.jneumeth.2022.109483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 02/07/2023]
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30
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Generative adversarial network and convolutional neural network-based EEG imbalanced classification model for seizure detection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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31
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Al-Hadeethi H, Abdulla S, Diykh M, Green JH. Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection. Diagnostics (Basel) 2021; 12:74. [PMID: 35054242 PMCID: PMC8774996 DOI: 10.3390/diagnostics12010074] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/21/2021] [Accepted: 12/25/2021] [Indexed: 11/17/2022] Open
Abstract
Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov-Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov-Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov-Smirnov (KST) and Mann-Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern-Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern-Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.
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Affiliation(s)
- Hanan Al-Hadeethi
- School of Sciences, University of Southern Queensland, Toowoomba, QLD 4300, Australia;
| | - Shahab Abdulla
- USQ College, University of Southern Queensland, Toowoomba, QLD 4300, Australia
| | - Mohammed Diykh
- College of Education for Pure Science, University of Thi-Qar, Nasiriyah 64001, Iraq
- Information and Communication Technology Research Group, Scientific Research Centre, Al-Ayen University, Nasiriyah 64001, Iraq
| | - Jonathan H. Green
- USQ College, University of Southern Queensland, Toowoomba, QLD 4300, Australia
- Faculty of the Humanities, University of the Free State, Bloemfontein 9301, South Africa
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32
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Duan L, Wang Z, Qiao Y, Wang Y, Huang Z, Zhang B. An Automatic Method for Epileptic Seizure Detection Based on Deep Metric Learning. IEEE J Biomed Health Inform 2021; 26:2147-2157. [PMID: 34962890 DOI: 10.1109/jbhi.2021.3138852] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of epilepsy which is a life-threatening neurological disorder. Many algorithms have been proposed for the automatic detection of epileptic seizures using traditional machine learning and deep learning. Although deep learning methods have achieved great success in many fields, their performance in EEG analysis and classification is still limited mainly due to the relatively small sizes of available datasets. In this paper, we propose an automatic method for the detection of epileptic seizures based on deep metric learning which is a novel strategy tackling the few-shot problem by mitigating the demand for massive data. First, two one-dimensional convolutional embedding modules are proposed as a deep feature extractor, for single-channel and multichannel EEG signals respectively. Then, a deep metric learning model is detailed along with a stage-wise training strategy. Experiments are conducted on the publicly-available Bonn University dataset which is a benchmark dataset, and the CHB-MIT dataset which is larger and more realistic. Impressive averaged accuracy of 98.60% and specificity of 100% are achieved on the most difficult classification of interictal (subset D) vs ictal (subset E) of the Bonn dataset. On the CHB-MIT dataset, an averaged accuracy of 86.68% and specificity of 93.71% are reached. With the proposed method, automatic and accurate detection of seizures can be performed in real time, and the heavy burden of neurologists can be effectively reduced.
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He C, Liu J, Zhu Y, Du W. Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review. Front Hum Neurosci 2021; 15:765525. [PMID: 34975434 PMCID: PMC8718399 DOI: 10.3389/fnhum.2021.765525] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/18/2021] [Indexed: 11/30/2022] Open
Abstract
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
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Affiliation(s)
- Chao He
- Shenzhen EEGSmart Technology Co., Ltd., Shenzhen, China
| | - Jialu Liu
- Shenzhen EEGSmart Technology Co., Ltd., Shenzhen, China
| | - Yuesheng Zhu
- School of Electronic and Computer Engineering, Peking University, Beijing, China
| | - Wencai Du
- Institute for Data Engineering and Sciences, University of Saint Joseph, Macao, Macao SAR, China
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Liu G, Tian L, Zhou W. Patient-Independent Seizure Detection Based on Channel-Perturbation Convolutional Neural Network and Bidirectional Long Short-Term Memory. Int J Neural Syst 2021; 32:2150051. [PMID: 34781854 DOI: 10.1142/s0129065721500519] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the massive burden caused by manual inspection of long-term EEG. At present, most seizure detection methods are highly patient-dependent and have poor generalization performance. In this study, a novel patient-independent approach is proposed to effectively detect seizure onsets. First, the multi-channel EEG recordings are preprocessed by wavelet decomposition. Then, the Convolutional Neural Network (CNN) with proper depth works as an EEG feature extractor. Next, the obtained features are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network to further capture the temporal variation characteristics. Finally, aiming to reduce the false detection rate (FDR) and improve the sensitivity, the postprocessing, including smoothing and collar, is performed on the outputs of the model. During the training stage, a novel channel perturbation technique is introduced to enhance the model generalization ability. The proposed approach is comprehensively evaluated on the CHB-MIT public scalp EEG database as well as a more challenging SH-SDU scalp EEG database we collected. Segment-based average accuracies of 97.51% and 93.70%, event-based average sensitivities of 86.51% and 89.89%, and average AUC-ROC of 90.82% and 90.75% are yielded on the CHB-MIT database and SH-SDU database, respectively.
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Affiliation(s)
- Guoyang Liu
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China.,Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Lan Tian
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China.,Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China.,Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
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Wang X, Wang X, Liu W, Chang Z, Kärkkäinen T, Cong F. One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.06.048] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Chakrabarti S, Swetapadma A, Pattnaik PK. A channel independent generalized seizure detection method for pediatric epileptic seizures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106335. [PMID: 34390934 DOI: 10.1016/j.cmpb.2021.106335] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by the World Health Organization. Electroencephalogram (EEG) is one of the most common and non-invasive ways of analyzing and studying the subtle changes in neuronal activity of the brain during an epileptic seizure attack. These changes can be analyzed for developing an automated system that would assert the chances of an impending seizure. As changeable nature of seizure affects the patients from having a normal life, hence progress in developing new methods will improve the quality of life and also provide assistance in the medical sector. Objective of the proposed method is to avoid EEG channel selection and use all input EEG channel features to design a generalized epileptic seizure detection framework. METHOD In this work, a long short-term memory network has been proposed that is not complex and has the capability of effectively detecting epileptic seizures from both non-invasive and invasive electroencephalogram recordings. The proposed framework is simple and effective and designed in such capacity that raw electroencephalogram signals can be used to detect seizures. Also, a generalized approach has been followed that is channel independent such that EEG signals belonging to any hemisphere of the brain can be detected effectively by the proposed architecture. RESULTS The automated seizure detection system achieved high seizure detection sensitivity of 99.9%, and a low false-positive rate of 0.003 per hour for the Children's Hospital Boston-Massachusetts Institute of Technology dataset. While for the Sleep-Wake-Epilepsy-Center of the University Department of Neurology at the Inselspital Bern dataset, the sensitivity is 99.4% and false-positive rate of 0.006 per hour. Convergence analysis of the proposed model provides a significant amount of reliability and correctness in the efficient detection of epileptic seizures. CONCLUSION Assessment of the proposed framework on non-invasive as well as invasive EEG signals showed that the framework worked well for different type of EEG recordings as different metrics gave satisfactory results. As the framework is simple and did not require any additional parameter optimization techniques, it reduced the processing overheads without affecting the accuracy. Hence, it can be used as an efficient method for monitoring epileptic seizures.
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Affiliation(s)
- Satarupa Chakrabarti
- School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Aleena Swetapadma
- School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India.
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Mittal R, Prince AA, Nalband S, Robert F, Fredo ARJ. Modified-MaMeMi filter bank for efficient extraction of brainwaves from electroencephalograms. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102927] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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A Study on Seizure Detection of EEG Signals Represented in 2D. SENSORS 2021; 21:s21155145. [PMID: 34372381 PMCID: PMC8348755 DOI: 10.3390/s21155145] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/25/2021] [Accepted: 07/26/2021] [Indexed: 12/11/2022]
Abstract
A seizure is a neurological disorder caused by abnormal neuronal discharges in the brain, which severely reduces the quality of life of patients and often endangers their lives. Automatic seizure detection is an important research area in the treatment of seizure and is a prerequisite for seizure intervention. Deep learning has been widely used for automatic detection of seizures, and many related research works decomposed the electroencephalogram (EEG) raw signal with a time window to obtain EEG signal slices, then performed feature extraction on the slices, and represented the obtained features as input data for neural networks. There are various methods for EEG signal decomposition, feature extraction, and representation, and most of the studies have been based on fixed hardware resources for the design of the scheme, which reduces the adaptability of the scheme in different application scenarios and makes it difficult to optimize the algorithms in the scheme. To address the above issues, this paper proposes a deep learning-based model for seizure detection, mainly characterized by the two-dimensional representation of EEG features and the scalability of neural networks. The model modularizes the main steps of seizure detection and improves the adaptability of the model to different hardware resource constraints, in order to increase the convenience of the algorithm optimization or the replacement of each module. The proposed model consists of five parts, and the model was tested using two epilepsy datasets separately. The experimental results showed that the proposed model has strong generality and good classification accuracy for seizure detection.
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Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Panahiazar M, Khozeimeh F, Zare A, Hosseini-Nejad H, Khosravi A, Atiya AF, Aminshahidi D, Hussain S, Rouhani M, Nahavandi S, Acharya UR. Epileptic Seizures Detection Using Deep Learning Techniques: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5780. [PMID: 34072232 PMCID: PMC8199071 DOI: 10.3390/ijerph18115780] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 02/06/2023]
Abstract
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
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Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | | | - Navid Ghassemi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran;
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, School of Medicine, University of California, San Francisco, CA 94143, USA;
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Hossein Hosseini-Nejad
- Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran;
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Amir F. Atiya
- Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo 12613, Egypt;
| | - Diba Aminshahidi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Sadiq Hussain
- System Administrator at Dibrugarh University, Assam 786004, India;
| | - Modjtaba Rouhani
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Udyavara Rajendra Acharya
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Taichung City 41354, Taiwan
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Ozdemir MA, Cura OK, Akan A. Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning. Int J Neural Syst 2021; 31:2150026. [PMID: 34039254 DOI: 10.1142/s012906572150026x] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.
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Affiliation(s)
- Mehmet Akif Ozdemir
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli 35620, Izmir, Turkey
| | - Ozlem Karabiber Cura
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli 35620, Izmir, Turkey
| | - Aydin Akan
- Department of Electrical and Electronics Eng., Izmir University of Economics, Balcova 35330, Izmir, Turkey
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Liu G, Xiao R, Xu L, Cai J. Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals. Front Syst Neurosci 2021; 15:685387. [PMID: 34093143 PMCID: PMC8173051 DOI: 10.3389/fnsys.2021.685387] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/20/2021] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.
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Affiliation(s)
- Guangda Liu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Ruolan Xiao
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Lanyu Xu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Jing Cai
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
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EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GAN. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00336-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractEEG-based emotion recognition has attracted substantial attention from researchers due to its extensive application prospects, and substantial progress has been made in feature extraction and classification modelling from EEG data. However, insufficient high-quality training data are available for building EEG-based emotion recognition models via machine learning or deep learning methods. The artificial generation of high-quality data is an effective approach for overcoming this problem. In this paper, a multi-generator conditional Wasserstein GAN method is proposed for the generation of high-quality artificial that covers a more comprehensive distribution of real data through the use of various generators. Experimental results demonstrate that the artificial data that are generated by the proposed model can effectively improve the performance of emotion classification models that are based on EEG.
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Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09986-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Lee SH. Classification of epileptic seizure using feature selection based on fuzzy membership from EEG signal. Technol Health Care 2021; 29:519-529. [PMID: 33682788 PMCID: PMC8158055 DOI: 10.3233/thc-218049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND: Feature selection is a technology that improves the performance result by eliminating overlapping or unrelated features. OBJECTIVE: To improve the performance result, this study proposes a new feature selection that uses the distance between the centers. METHODS: This study uses the distance between the centers of gravity (DBCG) of the bounded sum of the weighted fuzzy memberships (BSWFMs) supported by a neural network with weighted fuzzy membership (NEWFM). RESULTS: Using distance-based feature selection, 22 minimum features with a high performance result are selected, with the shortest DBCG of BSWFMs removed individually from the initial 24 features. The NEWFM used 22 minimum features as inputs to obtain a sensitivity, accuracy, and specificity of 99.3%, 99.5%, and 99.7%, respectively. CONCLUSIONS: In this study, only the mean DBCG is used to select the features; in the future, however, it will be necessary to incorporate statistical methods such as the standard deviation, maximum, and normal distribution.
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Craley J, Johnson E, Jouny C, Venkataraman A. Automated inter-patient seizure detection using multichannel Convolutional and Recurrent Neural Networks. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102360] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Gómez C, Arbeláez P, Navarrete M, Alvarado-Rojas C, Le Van Quyen M, Valderrama M. Automatic seizure detection based on imaged-EEG signals through fully convolutional networks. Sci Rep 2020; 10:21833. [PMID: 33311533 PMCID: PMC7732993 DOI: 10.1038/s41598-020-78784-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 11/26/2020] [Indexed: 02/06/2023] Open
Abstract
Seizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.
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Affiliation(s)
- Catalina Gómez
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, Colombia
- Center for Research and Formation in Artificial Intelligence (CINFONIA), Universidad de los Andes, Bogotá, Colombia
| | - Pablo Arbeláez
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, Colombia
- Center for Research and Formation in Artificial Intelligence (CINFONIA), Universidad de los Andes, Bogotá, Colombia
| | - Miguel Navarrete
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, Colombia
- School of Psychology, Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | | | - Michel Le Van Quyen
- Laboratoire d'Imagerie Biomédicale (LIB), Inserm U1146 / Sorbonne Université UMCR2 / UMR7371 CNRS, 15 rue de l'Ecole de Médecine, 75006, Paris, France
| | - Mario Valderrama
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, Colombia.
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Lashgari E, Liang D, Maoz U. Data augmentation for deep-learning-based electroencephalography. J Neurosci Methods 2020; 346:108885. [DOI: 10.1016/j.jneumeth.2020.108885] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 07/10/2020] [Accepted: 07/24/2020] [Indexed: 12/24/2022]
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GRP-DNet: A gray recurrence plot-based densely connected convolutional network for classification of epileptiform EEG. J Neurosci Methods 2020; 347:108953. [PMID: 33007344 DOI: 10.1016/j.jneumeth.2020.108953] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/16/2020] [Accepted: 09/16/2020] [Indexed: 12/27/2022]
Abstract
BACKGROUND The classification of epileptiform electroencephalogram (EEG) signals has been treated as an important but challenging issue for realizing epileptic seizure detection. In this work, combing gray recurrence plot (GRP) and densely connected convolutional network (DenseNet), we developed a novel classification system named GRP-DNet to identify seizures and epilepsy from single-channel, long-term EEG signals. NEW METHODS The proposed GRP-DNet classification system includes three main modules: 1) input module takes an input long-term EEG signal and divides it into multiple short segments using a fixed-size non-overlapping sliding window (FNSW); 2) conversion module transforms short segments into GRPs and passes them to the DenseNet; 3) fusion and decision, the predicted label of each GRP is fused using a majority voting strategy to make the final decision. RESULTS Six different classification experiments were designed based on a publicly available benchmark database to evaluate the effectiveness of our system. Experimental results showed that the proposed GRP-DNet achieved an excellent classification accuracy of 100 % in each classification experiment, Furthermore, GRP-DNet gave excellent computational efficiency, which indicates its tremendous potential for developing an EEG-based online epilepsy diagnosis system. COMPARISON WITH EXISTING METHODS Our GRP-DNet system was superior to the existing competitive classification systems using the same database. CONCLUSIONS The GRP-DNet is a potentially powerful system for identifying and classifying EEG signals recorded from different brain states.
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Scalp EEG classification using deep Bi-LSTM network for seizure detection. Comput Biol Med 2020; 124:103919. [DOI: 10.1016/j.compbiomed.2020.103919] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 06/29/2020] [Accepted: 07/14/2020] [Indexed: 11/15/2022]
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