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A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals

Wei Zhao, Wenbing Zhao, Wenfeng Wang, Xiaolu Jiang, Xiaodong Zhang, Yonghong Peng, Baocan Zhang, Guokai Zhang
2020 Computational and Mathematical Methods in Medicine  
To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers.  ...  The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures.  ...  Conclusion A novel model for robust detection of seizures has been proposed, which deals with two-class, three-class, and fiveclass classification problems. e proposed approach has been developed based  ... 
doi:10.1155/2020/9689821 pmid:32328157 pmcid:PMC7166278 fatcat:ktola2ohxvchvaapziwus5rrjm

Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection using High-dimension EEG Signals

Yuan Liu, Yu-Xuan Huang, Xuexi Zhang, Wen Qi, Jing Guo, Yingbai Hu, Longbin Zhang, Hang Su
2020 IEEE Access  
In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close).  ...  Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually.  ...  In this paper, we proposed a novel deep C-LSTM neural network structure for epileptic seizure and tumor detection.  ... 
doi:10.1109/access.2020.2976156 fatcat:r4da7v4d6rdaxe5lmo5vfivlpy

Epileptic Seizure Detection: A Deep Learning Approach [article]

Ramy Hussein, Hamid Palangi, Rabab Ward, Z. Jane Wang
2018 arXiv   pre-print
To address these challenges, we introduce the use of a deep learning-based approach that automatically learns the discriminative EEG features of epileptic seizures.  ...  Second, Long Short-Term Memory (LSTM) network is used to learn the high-level representations of the normal and the seizure EEG patterns.  ...  CONCLUSION In this paper, we introduce a deep learning approach for the automatic detection of epileptic seizures using EEG signals.  ... 
arXiv:1803.09848v1 fatcat:uhxdxmoab5bdvcziuwuiew7hyi

A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures [article]

X. Yao, X. Li, Q. Ye, Y. Huang, Q. Cheng, G.-Q. Zhang
2019 arXiv   pre-print
Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy.  ...  Manual seizure identification on EEG by trained neurologists is time-consuming, labor-intensive and error-prone, and a reliable automatic seizure/non-seizure classification method is needed.  ...  In [17], Acharya et al. present a 13-layers deep neural network for seizure detection by using convolutional neural network (CNN), which is called CNN approach.  ... 
arXiv:1812.06562v2 fatcat:taqlp6qcofdyfntlf7njfk52me

Epileptic Seizure Detection using Deep Learning Approach

Sirwan Tofiq Jaafar, Mokhtar Mohammadi
2019 UHD Journal of Science and Technology  
An epileptic seizure is a sign of abnormal activity in the human brain. Electroencephalogram (EEG) is a standard tool that has been used vastly for detection of seizure activities.  ...  The EEG signal is segmented into 4 s segments and used to train the long- and short-term memory network. The trained model is used to discriminate the EEG seizure from the background.  ...  Several attempts have been made to develop an automatic epileptic seizure detection method for classifying EEG signals using the deep neural network in which the features are extracted automatically.  ... 
doi:10.21928/uhdjst.v3n2y2019.pp41-50 fatcat:rupuw2sufbehncguhyp4aacvfe

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Maryam Panahiazar, Fahime Khozeimeh, Assef Zare, Hossein Hosseini-Nejad, Abbas Khosravi, Amir F. Atiya (+5 others)
2021 International Journal of Environmental Research and Public Health  
The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed.  ...  A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities.  ...  This class of DL networks is widely used for the detection of epileptic seizures using EEG signals.  ... 
doi:10.3390/ijerph18115780 pmid:34072232 fatcat:vdok6mql4rfxln737tjb23ufte

SeizureNet: a model for automatic detection of epileptic seizures using EEG signals based on convolutional neural network

Wei Zhao, Wenfeng Wang
2020 Cognitive Computation and Systems  
To address this issue, the authors present a novel model, named SeizureNet, for robust detection of epileptic seizures using EEG signals based on convolutional neural network.  ...  Epilepsy is a neurological disorder and generally detected by electroencephalogram (EEG) signals. The manual inspection of epileptic seizures is a time-consuming and laborious process.  ...  Conclusion This study introduces a new model for the robust detection of epileptic seizures using EEG signals based on CNN.  ... 
doi:10.1049/ccs.2020.0011 fatcat:upoe3nocavhfvasdmnu5fhsewm

Epileptic seizure detection using deep learning techniques: A Review [article]

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Mahboobeh Jafari, Sadiq Hussain, Roohallah Alizadehsani, Parisa Moridian, Abbas Khosravi, Hossein Hosseini-Nejad, Modjtaba Rouhani, Assef Zare, Ali Khadem (+3 others)
2020 arXiv   pre-print
A variety of screening approaches have been proposed to diagnose epileptic seizures, using Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) modalities.  ...  In this study, a comprehensive overview of the types of deep learning methods exploited to diagnose epileptic seizures from various modalities has been studied.  ...  This class of deep learning networks is widely used for the detection of epileptic seizures using EEG signals.  ... 
arXiv:2007.01276v2 fatcat:2mfgpishmrculocm32ui7iemym

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection [article]

Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li
2020 arXiv   pre-print
Methods: A complex deep neural network model is proposed to learn the pure seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training.  ...  step toward the development of large-scale deployment for real-life use.  ...  To overstep this challenge, we proposed a novel decomposition model based on deep neural networks.  ... 
arXiv:1909.10868v2 fatcat:bsdfbhwkrna2vixujfnq46w7wq

An Overview of Deep Learning Techniques for Epileptic Seizures Detection and Prediction Based on Neuroimaging Modalities: Methods, Challenges, and Future Works [article]

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Yinan Kong, Juan Manuel Gorriz, Javier Ramírez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya
2022 arXiv   pre-print
This paper has studied a comprehensive overview of DL methods exploited for epileptic seizures detection and prediction using neuroimaging modalities.  ...  Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included.  ...  A comparison of deep neural networks for seizure detection in eeg signals. bioRxiv, Fergus, P., Hignett, D., Hussain, A., Al-Jumeily, D., and Abdel-Aziz, K. (2015).  ... 
arXiv:2105.14278v2 fatcat:dxv3nkbyajetjokhf5eitttpgi

Guest Editorial: Current Trends in Cognitive Science and Brain Computing Research and Applications

Varun Bajaj, G R Sinha, Siuly Siuly, Abdulkadir Şengur
2020 Electronics Letters  
The article by Ari entitled 'Analysis of EEG signal for seizure detection based on WPT' suggests a design to create a computer-based expert system for the detection of epilepsy.  ...  In another interesting article, 'A novel approach based on wavelet packet transform and 1d-RMLBP for drowsiness detection using EEG', Alçin reports an EEG based approach for detection of drowsiness.  ...  The article by Ari entitled 'Analysis of EEG signal for seizure detection based on WPT' suggests a design to create a computer-based expert system for the detection of epilepsy.  ... 
doi:10.1049/el.2020.2790 fatcat:tyub3uxoyngivddcg7f6esce4y

A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy

Ahmed Abdelhameed, Magdy Bayoumi
2021 Frontiers in Computational Neuroscience  
Therefore, this article proposes a novel deep-learning approach for detecting seizures in pediatric patients based on the classification of raw multichannel EEG signal recordings that are minimally pre-processed  ...  The new approach takes advantage of the automatic feature learning capabilities of a two-dimensional deep convolution autoencoder (2D-DCAE) linked to a neural network-based classifier to form a unified  ...  which all lack the proper statistical analysis for significance testing.CONCLUSIONA novel deep-learning approach for the detection of seizures in pediatric patients is proposed.The novel approach uses  ... 
doi:10.3389/fncom.2021.650050 pmid:33897397 pmcid:PMC8060463 fatcat:barwq65uw5ctxb54cmrlichcou

Epileptic seizure detection using deep learning through min max scaler normalization

B Deepa, K Ramesh
2022 International Journal of Health Sciences  
Machine learning and deep learning have allowed us to analyze brain signals with high accuracy. The brain signals collected using EEG (electroencephalogram) are complex and prone to noise.  ...  Epileptic seizure detection and prediction are significantly sought-after research currently because robust algorithms are available.  ...  The Research Work titled "Detection and Prediction of Epileptic Seizure Using Advanced Algorithms" is awarded Karnataka DST-Ph.D. fellowship from KSTePS, Department of Science and Technology (DST), Govt  ... 
doi:10.53730/ijhs.v6ns1.7801 fatcat:cykusycujracfiojyj7qxyqjc4

Gated Recurrent Networks for Seizure Detection [article]

Meysam Golmohammadi, Saeedeh Ziyabari, Vinit Shah, Eva Von Weltin, Christopher Campbell, Iyad Obeid, Joseph Picone
2018 arXiv   pre-print
These are evaluated using a state of the art hybrid architecture that integrates Convolutional Neural Networks (CNNs) with RNNs.  ...  A significant big data resource, known as the TUH EEG Corpus (TUEEG), has recently become available for EEG research, creating a unique opportunity to evaluate these recurrent units on the task of seizure  ...  Using a subset of this data that has been manually annotated for seizure events [13] , a novel deep structure has been recently introduced which achieves a low false alarm rate on EEG signals [14] .  ... 
arXiv:1801.02471v1 fatcat:fcebnqofxzh73oyynfp3vtqwlm

Neonatal Seizure Detection using Convolutional Neural Networks [article]

Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
2017 arXiv   pre-print
This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection.  ...  The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector.  ...  The use of a fully convolutional neural network (FCNN), without any fully connected layers at the back-end, allows for the application of the developed seizure detector to a segment of EEG of any length  ... 
arXiv:1709.05849v1 fatcat:p2wh6ptoprf4rb5kilwoligyz4
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