A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Machine learning applications for electroencephalograph signals in epilepsy: a quick review
2020
Acta Epileptologica
Preserved Fulltext
The present review examines various ML approaches for electroencephalograph (EEG) signal procession in epilepsy research, highlighting applications in the aspect of automated seizure detection, prediction ...
The present review also presents advantage, challenge and future direction of ML techniques in the analysis of EEG signals in epilepsy. ...
In Tsiouris's study long short-term memory networks were introduced for seizure prediction in EEG signals [48] . ...
doi:10.1186/s42494-020-00014-0
fatcat:xoqhcoppjbfepho7q6ltekbljm
Automatic seizure detection based on imaged-EEG signals through fully convolutional networks
2020
Scientific Reports
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We used fully convolutional neural networks to automatically detect seizures. ...
This process could be extensive, inefficient and time-consuming, especially for long term recordings. ...
We further introduced two regularization strategies for Deep Neural Networks. ...
doi:10.1038/s41598-020-78784-3
pmid:33311533
pmcid:PMC7732993
fatcat:tgigm3ldybfs7k4akigcow6zge
Seizure Prediction in EEG Signals Using STFT and Domain Adaptation
2022
Frontiers in Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is text/html.
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. ...
Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches. ...
Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. ...
doi:10.3389/fnins.2021.825434
pmid:35115906
pmcid:PMC8805457
fatcat:z4kqotvzgzhfbgyvrgjvldocpi
Seizure Prediction With HIVE-CODAs: The Hierarchical Vote Collective of Domain Adaptation Methods
2022
Frontiers in Physics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is text/html.
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. ...
Experimental results on both intracranial and scalp EEG databases demonstrate that this method can reduce the domain gap effectively compared with previous studies. ...
A Long Short-Term Memory Deep Learning Network for the Prediction of Epileptic Seizures Using EEG Signals. ...
doi:10.3389/fphy.2021.811681
fatcat:gkhqxnfrpjcjrbfx44ltdoh36i
Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG
2017
IEEE transactions on neural systems and rehabilitation engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Index Terms-Convolutional neural networks, epilepsy detection, intracranial EEG, multi score class learning. A. Antoniades, S. Sanei and C. Cheong Took are with the ...
Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. ...
CONVOLUTIONAL NEURAL NETWORKS FOR EEG LEARNING Based on neural networks and the convolution operation, CNNs perform convolution at each convolutional hidden layer between the input and the weights. ...
doi:10.1109/tnsre.2017.2755770
pmid:28952945
fatcat:i5qv6fkf7zd5fktpltihwod5ze
Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States
2019
Scientific Reports
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. ...
Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction. ...
Acknowledgements Authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada, Epilepsy Canada and the Institute for Data Valorization (IVADO). ...
doi:10.1038/s41598-019-52152-2
pmid:31666621
pmcid:PMC6821856
fatcat:63mv5lizyzgb7jicwl5j6nvkz4
Neural Memory Networks for Seizure Type Classification
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We show that our model achieves a state-of-the-art weighted F1 score of 0.945 for seizure type classification on the TUH EEG Seizure Corpus with the IBM TUSZ preprocessed data. ...
We first explore the performance of traditional deep learning techniques which use convolutional and recurrent neural networks, and enhance these architectures by using external memory modules with trainable ...
One of the main limitations of using traditional recurrent neural networks such as Long Short Term Memory (LSTM) [21] or Gated Recurrent Unit (GRU) [22] layers with seizure recordings is that they ...
arXiv:1912.04968v2
fatcat:oerbc7bx2rcw7fajxoush4hesq
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
2021
International Journal of Environmental Research and Public Health
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. ...
Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. ...
Convolutional Recurrent Neural Networks (CNN-RNNs) The highly efficient combination of DL networks used to predict and detect epileptic seizures from EEG signals is the CNN-RNN architecture. ...
doi:10.3390/ijerph18115780
pmid:34072232
pmcid:PMC8199071
fatcat:vdok6mql4rfxln737tjb23ufte
Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels
2022
Journal of Personalized Medicine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels. ...
Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. ...
In this study, we aimed to classify seizures using preictal EEGs based on a hybrid model that combines a onedimensional convolutional neural network (1D CNN) and a gated recurrent unit (GRU). ...
doi:10.3390/jpm12050763
pmid:35629185
pmcid:PMC9147609
fatcat:5vo3gy7io5h25jjyatax5vxccy
Multi-Channel Vision Transformer for Epileptic Seizure Prediction
2022
Biomedicines
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity ...
of 99.80% for surface EEG and 90.28–91.15% for invasive EEG data. ...
Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 2018, 105, 104-111. [CrossRef] [PubMed] 25. ...
doi:10.3390/biomedicines10071551
pmid:35884859
pmcid:PMC9312955
fatcat:7nuljdhtfnb7tok3p4xfdblapm
Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure
2023
Diagnostics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
We have developed a deep learning model using Deep convolutional Autoencoder—Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using ...
EEG data. ...
A single convolutional layer has filters (kernels) with trainable
Bidirectional Long Short-Term Memory Bidirectional Long Short-Term Memory is inspired from Bi directional Recurrent Neural Networks that ...
doi:10.3390/diagnostics13040773
pmid:36832260
pmcid:PMC9954819
fatcat:35exhor57fbvzotfrrsxwesuwm
Deep Convolutional Neural Network Based Interictal-Preictal Electroencephalography Prediction: Application to Focal Cortical Dysplasia Type-II
2020
Frontiers in Neurology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We performed three consecutive interictal-preictal classification steps by varying the preictal length, number of electrodes, and sampling frequency with convolutional neural networks (CNN) using 30 s ...
CNN-based classifiers from intracranial EEG recordings using a small number of electrodes and efficient sampling frequency are feasible for predicting the interictal-preictal state transition preceding ...
For our next steps, we plan to evaluate the predictability of seizure prediction systems based on our deep learning-based classification models using long-term intracranial EEG recordings in a larger number ...
doi:10.3389/fneur.2020.594679
pmid:33250854
pmcid:PMC7674929
fatcat:xkf6mnihm5a7rnnqiao3lzoexu
DETECTION OF EPILEPTIC SEIZURES USING EEG SIGNALS
[article]
2023
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
Inan extensive experimental analysis, we validated the system with a real-time EEG dataset, where weobtained 82.5% accuracy for epilepsy detection for the entire testing dataset. ...
In the convolutional layer, numerous features areextracted from EEG signal files, while in the optimization layer, non-essential elements are eliminated. ...
This study shows how Convolutional Neural Networks and Long Short-Term Memory can combine to classify EEG data completely. ...
doi:10.5281/zenodo.10491863
fatcat:6wrcqlb3cvc5lhgwtpjtp3mbja
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this work, we develop a convolutional network module that exploits Electroencephalogram (EEG) scalograms to distinguish between the pre-seizure and normal brain activities. ...
Results show that our proposed solution outperforms the state-of-the-art methods, achieving seizure prediction sensitivity scores of 88.45% and 89.52% for the American Epilepsy Society and Melbourne University ...
This motivated researchers to use deep neural networks for building automated seizure prediction solutions. ...
arXiv:2007.11716v1
fatcat:43bbfo2dhrhltdnbzyjor7mwdu
Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network
2021
Frontiers in Neurology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. ...
Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). ...
The network contains 11 residual blocks with 2 one-dimensional convolutional layers per block. The convolutional kernel size is 16. ...
doi:10.3389/fneur.2021.603868
pmid:34012415
pmcid:PMC8126697
fatcat:mxbpnbfscfbm7pcw6xbmncbhwi
« Previous
Showing results 1 — 15 out of 209 results