20.9.10 Epileptic Seizures, Epilepsy Detection and Analysis

Chapter Contents (Back)
Epilepsy. Epileptic Seizures.

Lee, J.S.[Jae Sung], Lee, D.S.[Dong Soo], Kim, S.K.[Seok-Ki], Lee, S.K.[Sang-Kun], Chung, J.K.[June-Key], Lee, M.C.[Myung Chul], Park, K.S.[Kwang Suk],
Localization of epileptogenic zones in F-18 FDG brain PET of patients with temporal lobe epilepsy using artificial neural network,
MedImg(19), No. 4, April 2000, pp. 347-355.
IEEE Top Reference. 0110
BibRef

Baete, K., Nuyts, J., van Paesschen, W., Suetens, P., Dupont, P.,
Anatomical-based FDG-PET reconstruction for the detection of hypo-metabolic regions in epilepsy,
MedImg(23), No. 4, April 2004, pp. 510-519.
IEEE Abstract. 0406
BibRef

Unsworth, C.P., Spowart, J.J., Lawson, G., Brown, J.K., Mulgrew, B., Minns, R.A., Clark, M.,
New hypothesis test: a repropagation method to test the applicability of linear ICA to a given problem (highlighted by an EEG case study applied to epilepsy),
VISP(152), No. 5, October 2005, pp. 545-552.
DOI Link 0512
BibRef

DeLorenzo, C.[Christine], Papademetris, X.[Xenophon], Staib, L.H.[Lawrence H.], Vives, K.P.[Kenneth P.], Spencer, D.D.[Dennis D.], Duncan, J.S.[James S.],
Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery,
MedImg(29), No. 2, February 2010, pp. 322-338.
IEEE DOI 1002
BibRef
Earlier:
Nonrigid Intraoperative Cortical Surface Tracking Using Game Theory,
MMBIA07(1-8).
IEEE DOI 0710

See also Local Shape Registration Using Boundary-Constrained Match of Skeletons.
See also Estimation of 3-d left ventricular deformation from medical images using biomechanical models. BibRef

Vazquez, D.M., Rubio, J.J., Pacheco, J.,
Characterisation framework for epileptic signals,
IET-IPR(6), No. 9, 2012, pp. 1227-1235.
DOI Link 1302
BibRef

Wang, J.[Jing], Gao, X.Z., Guo, P.[Ping],
Feature extraction based on sparse representation with application to epileptic EEG classification,
IJIST(23), No. 2, 2013, pp. 104-113.
DOI Link 1307
electroencephalogram signals, epilepsy seizures, seizure detection, Bayesian decision rule BibRef

Kumar, Y.[Yatindra], Dewal, M.L., Anand, R.S.,
Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network,
SIViP(8), No. 7, October 2014, pp. 1323-1334.
WWW Link. 1410
BibRef

Wang, Y.M.[Yue-Ming], Qi, Y.[Yu], Zhu, J.M.[Jun-Ming], Zhang, J.M.[Jian-Min], Wang, Y.W.[Yi-Wen], Zheng, X.X.[Xiao-Xiang], Wu, Z.H.[Zhao-Hui],
A Cauchy-Based State-Space Model for Seizure Detection in EEG Monitoring Systems,
IEEE_Int_Sys(30), No. 1, January 2015, pp. 6-12.
IEEE DOI 1502
Biomedical monitoring BibRef

Das, A.B.[Anindya Bijoy], Bhuiyan, M.I.H.[Mohammed Imamul Hassan], Alam, S.M.S.[S. M. Shafiul],
Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection,
SIViP(10), No. 1, February 2016, pp. 259-266.
Springer DOI 1601
BibRef

Erem, B., Hyde, D.E., Peters, J.M., Duffy, F.H., Warfield, S.K.,
Dynamic Electrical Source Imaging (DESI) of Seizures and Interictal Epileptic Discharges Without Ensemble Averaging,
MedImg(36), No. 1, January 2017, pp. 98-110.
IEEE DOI 1701
Brain modeling BibRef

Deivasigamani, S., Senthilpari, C., Yong, W.H.[Wong Hin],
Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection,
IJIST(26), No. 4, 2016, pp. 277-283.
DOI Link 1701
focal signals, epilepsy, epileptogenic area, wavelet, brain BibRef

Sharma, M.[Manish], Pachori, R.B.[Ram Bilas], Acharya, U.R.[U. Rajendra],
A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension,
PRL(94), No. 1, 2017, pp. 172-179.
Elsevier DOI 1708
Electroencephalography BibRef

Chen, S.N.[Sha-Nen], Zhang, X.[Xi], Yang, Z.X.[Zhi-Xian],
Epileptic seizure detection by combining robust-principal component analysis and least square-support vector machine,
IJIST(27), No. 4, 2017, pp. 368-375.
DOI Link 1712
electroencephalogram, least square-support vector machine, maximum cross-correlation, seizure detection BibRef

Guo, J., Yang, K., Liu, H., Yin, C., Xiang, J., Li, H., Ji, R., Gao, Y.,
A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy,
MedImg(37), No. 11, November 2018, pp. 2474-2482.
IEEE DOI 1811
Hafnium oxide, Detectors, Feature extraction, Machine learning, Surgery, Epilepsy, Head, High-frequency oscillations, MEG, SSAE, brain, detector BibRef

Jothiraj, S.N.[Sairamya Nanjappan], Selvaraj, T.G.[Thomas George], Ramasamy, B.[Balakrishnan], Deivendran, N.P.[Narain Ponraj], Subathra, M.S.P.,
Classification of EEG signals for detection of epileptic seizure activities based on feature extraction from brain maps using image processing algorithms,
IET-IPR(12), No. 12, December 2018, pp. 2153-2162.
DOI Link 1812
BibRef

Yan, M.[Ming], Liu, L.[Ling], Basodi, S.[Sunitha], Pan, Y.[Yi],
Multi-view learning for benign epilepsy with centrotemporal spikes,
IET-CV(13), No. 2, March 2019, pp. 109-116.
DOI Link 1902
BibRef

Onofrey, J.A., Staib, L.H., Papademetris, X.,
Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning,
MedImg(38), No. 2, February 2019, pp. 596-607.
IEEE DOI 1902
Computed tomography, Image segmentation, Brain, Electrodes, Magnetic resonance imaging, Surgery, Segmentation, epilepsy BibRef

Xie, L., Deng, Z., Xu, P., Choi, K., Wang, S.,
Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals,
Cyber(49), No. 6, June 2019, pp. 2200-2214.
IEEE DOI 1904
Brain modeling, Electroencephalography, Fuzzy neural networks, Training, Data models, Learning systems, transductive transfer learning BibRef

Xu, H., Dong, M., Lee, M., O'Hara, N., Asano, E., Jeong, J.,
Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery Using Diffusion Tractography and Convolutional Neural Networks,
MedImg(38), No. 8, August 2019, pp. 1910-1922.
IEEE DOI 1908
White matter, Epilepsy, Pediatrics, Surgery, Functional magnetic resonance imaging, Visualization, epilepsy surgery BibRef

Thara, D.K., Prema Sudha, B., Fan, X.[Xiong],
Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques,
PRL(128), 2019, pp. 544-550.
Elsevier DOI 1912
Deep neural network, Epilepsy, Seizure, Feature scaling, Loss BibRef

Thara, D.K., Prema Sudha, B., Fan, X.[Xiong],
Epileptic seizure detection and prediction using stacked bidirectional long short term memory,
PRL(128), 2019, pp. 529-535.
Elsevier DOI 1912
Epilepsy, Seizure, LSTM, Detection, Prediction, Deep learning BibRef

Abdelhameed, A.M., Bayoumi, M.,
Semi-Supervised EEG Signals Classification System for Epileptic Seizure Detection,
SPLetters(26), No. 12, December 2019, pp. 1922-1926.
IEEE DOI 2001
Electroencephalography, Feature extraction, Deep learning, Epilepsy, Training, Classification, cross-validation, deep learning, variational autoencoder BibRef

Ramos-Aguilar, R.[Ricardo], Olvera-López, J.A.[J. Arturo], Olmos-Pineda, I.[Ivan], Sánchez-Urrieta, S.[Susana],
Feature extraction from EEG spectrograms for epileptic seizure detection,
PRL(133), 2020, pp. 202-209.
Elsevier DOI 2005
EEG signals, Short-time Fourier Transform, Spectrograms, Feature Extraction, Seizure Classification BibRef

Abdulhay, E.[Enas], Elamaran, V., Chandrasekar, M., Balaji, V.S., Narasimhan, K.,
Automated diagnosis of epilepsy from EEG signals using ensemble learning approach,
PRL(139), 2020, pp. 174-181.
Elsevier DOI 2011
Classification, EEG signal, Entropy, Epilepsy, Higher order spectra BibRef

Ashokkumar, S.R., MohanBabu, G., Anupallavi, S.,
A novel two-band equilateral wavelet filter bank method for an automated detection of seizure from EEG signals,
IJIST(30), No. 4, 2020, pp. 978-993.
DOI Link 2011
classification, electroencephalogram, epilepsy, optimal equilateral wavelet filter banks, seizure BibRef

Wu, M.[Min], Wan, T.[Ting], Wan, X.[Xiongbo], Fang, Z.L.[Ze-Lin], Du, Y.X.[Yu-Xiao],
A new localization method for epileptic seizure onset zones based on time-frequency and clustering analysis,
PR(111), 2021, pp. 107687.
Elsevier DOI 2012
Epilepsy, Seizure onset zones, High-frequency oscillations, Time-frequency analysis, Clustering analysis BibRef

Malek, A.G.[Amirreza Geran], Mansoori, M.[Mojtaba], Omranpour, H.[Hesam],
Random forest and rotation forest ensemble methods for classification of epileptic EEG signals based on improved 1D-LBP feature extraction,
IJIST(31), No. 1, 2021, pp. 189-203.
DOI Link 2102
1D-local binary patterns, EEG classification, ensemble classification, epilepsy, feature extraction, rotation forest BibRef

Nasiri, S., Clifford, G.D.,
Generalizable Seizure Detection Model Using Generating Transferable Adversarial Features,
SPLetters(28), 2021, pp. 568-572.
IEEE DOI 2104
Training, Feature extraction, Electroencephalography, Brain modeling, Robustness, Electrodes, Deep learning, EEG, transferable features BibRef

Srinath, R.[Rajagopalan], Gayathri, R.[Rajagopal],
Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods,
IJIST(31), No. 2, 2021, pp. 729-740.
DOI Link 2105
ANFIS, EEG, epilepsy diagnosis, NSCT, soft computing BibRef

Ashokkumar, S.R., Anupallavi, S., Premkumar, M., Jeevanantham, V.,
Implementation of deep neural networks for classifying electroencephalogram signal using fractional S-transform for epileptic seizure detection,
IJIST(31), No. 2, 2021, pp. 895-908.
DOI Link 2105
deep neural networks, electroencephalogram, entropy, epilepsy, fractional S-transform BibRef

Mandal, S.[Sunandan], Singh, B.K.[Bikesh Kumar], Thakur, K.[Kavita],
Majority voting-based hybrid feature selection in machine learning paradigm for epilepsy detection using EEG,
IJCVR(11), No. 4, 2021, pp. 385-400.
DOI Link 2108
BibRef


Hussein, R.[Ramy], Lee, S.[Soojin], Ward, R.[Rabab], McKeown, M.J.[Martin J.],
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture,
ICPR21(5436-5443)
IEEE DOI 2105
Time-frequency analysis, Sensitivity, Image resolution, Convolution, Shape, Epilepsy, Machine learning BibRef

Chen, X., Zheng, Y., Niu, Y., Li, C.,
Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN,
CVIDL20(701-706)
IEEE DOI 2102
convolutional neural nets, electroencephalography, medical disorders, medical signal processing, neurophysiology, Biosignal processing BibRef

Aminpour, A.[Azad], Ebrahimi, M.[Mehran], Widjaja, E.[Elysa],
Lesion Localization in Paediatric Epilepsy Using Patch-based Convolutional Neural Network,
ICIAR20(II:216-227).
Springer DOI 2007
BibRef

Hoyos-Osorio, K.[Keider], Álvarez, A.M.[Andrés M.], Orozco, Á.A.[Álvaro A.], Rios, J.I.[Jorge I.], Daza-Santacoloma, G.[Genaro],
Clustering-Based Undersampling to Support Automatic Detection of Focal Cortical Dysplasias,
CIARP17(298-305).
Springer DOI 1802
BibRef

Beserra, F.S.[Fernando S.], Raimundo, M.M.[Marcos M.], von Zuben, F.J.[Fernando J.],
Ensembles of Multiobjective-Based Classifiers for Detection of Epileptic Seizures,
CIARP17(575-583).
Springer DOI 1802
BibRef

Farazi, M., Soltanian-Zadeh, H.,
Shape analysis of hippocampus in temporal lobe epilepsy using Signed Poisson Mapping,
IPRIA17(118-122)
IEEE DOI 1712
biomedical MRI, computational geometry, diseases, image classification, learning (artificial intelligence), temporal lobe epilepsy BibRef

Dasgupta, A.[Abhijit], Nayak, L.[Losiana], Das, R.[Ritankar], Basu, D.[Debasis], Chandra, P.[Preetam], De, R.K.[Rajat K.],
Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG Data,
PReMI17(87-95).
Springer DOI 1711
BibRef

Quintero-Rincón, A., Prendes, J., Pereyra, M., Batatia, H., Risk, M.,
Multivariate Bayesian classification of epilepsy EEG signals,
IVMSP16(1-5)
IEEE DOI 1608
Bayes methods BibRef

Sathyanarayana, S.[Supriya], Satzoda, R.K.[Ravi Kumar], Sathyanarayana, S.[Suchitra], Thambipillai, S.[Srikanthan],
Identifying epileptic seizures based on a template-based eyeball detection technique,
ICIP15(4689-4693)
IEEE DOI 1512
epileptic seizure; eyeball detection; vision-based monitoring BibRef

Boubchir, L.[Larbi], Al-Maadeed, S.[Somaya], Bouridane, A.[Ahmed], Cherif, A.A.[Arab Ali],
Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images,
ICIP15(3758-3762)
IEEE DOI 1512
EEG BibRef

Mahmoudi, F.[Fariborz], Nazem-Zadeh, M.R.[Mohammad-Reza], Bagher-Ebadian, H.[Hassan], Schwalb, J.M.[Jason M.], Soltanian-Zadeh, H.[Hamid],
Roles of Various Brain Structures on Non-Invasive Lateralization of Temporal Lobe Epilepsy,
ISVC14(II: 32-40).
Springer DOI 1501
BibRef

da Silva, N.M.[Nádia Moreira], Rego, R.[Ricardo], Silva Cunha, J.P.[João Paulo],
3D Multimodal Visualization of Subdural Electrodes with Cerebellum Removal to Guide Epilepsy Resective Surgery Procedures,
ICIAR14(II: 167-174).
Springer DOI 1410
BibRef

Ahmad, M.A.[Malik Anas], Khan, N.A.[Nadeem Ahmad], Majeed, W.[Waqas],
Computer Assisted Analysis System of Electroencephalogram for Diagnosing Epilepsy,
ICPR14(3386-3391)
IEEE DOI 1412
Accuracy BibRef

Shaker, M.[Matineh], Soltanian-Zadeh, H.[Hamid],
Voxel-Based Morphometric Study of Brain Regions from Magnetic Resonance Images in Temporal Lobe Epilepsy,
Southwest08(209-212).
IEEE DOI 0803
BibRef

Shen, T.W.[Tsu-Wang], Kuo, X.[Xavier], Yu, C.S.[Chung-Shan],
Intelligence computing approach for seizure detection based on intracranial electroencephalogram (IEEG),
ICPR08(1-4).
IEEE DOI 0812
BibRef

Sahin, C.[Cenk], Ogulata, S.N.[Seyfettin Noyan], Aslan, K.[Kezban], Bozdemir, H.[Hacer],
The Application of Neural Networks in Classification of Epilepsy Using EEG Signals,
BVAI07(499-508).
Springer DOI 0710
BibRef

Jing, M.[Min], Sanei, S.[Saeid],
Scanner Artifact Removal in Simultaneous EEG-fMRI for Epileptic Seizure Prediction,
ICPR06(III: 722-725).
IEEE DOI 0609
BibRef

Fernandes, J.M.[José Maria], Leal, A.[Alberto], Silva Cunha, J.P.[João Paulo],
EpiGauss: Spatio-temporal Characterization of Epiletogenic Activity Applied to Hypothalamic Hamartomas,
ICIAR06(II: 680-690).
Springer DOI 0610
BibRef

Duchesne, S., Bernasconi, N., Bernasconi, A., Collins, D.L.,
On the classification of temporal lobe epilepsy using MR image appearance,
ICPR02(I: 520-523).
IEEE DOI 0211
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Heart, Cardiac Applications .


Last update:Sep 12, 2021 at 22:38:33