Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizu...

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Published inJournal of personalized medicine Vol. 11; no. 10; p. 1028
Main Authors Prasanna, J., Subathra, M. S. P., Mohammed, Mazin Abed, Damaševičius, Robertas, Sairamya, Nanjappan Jothiraj, George, S. Thomas
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 15.10.2021
MDPI
Subjects
Accuracy
Automation
Classification
Convulsions & seizures
Diagnosis
Discriminant analysis
EEG
Electroencephalography
Epilepsy
Feature selection
Machine learning
Patients
Pediatrics
Precision medicine
Review
Seizures
Signal processing
Support vector machines
Wavelet transforms
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Abstract Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.
AbstractList Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.
Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.
Author Prasanna, J.
Damaševičius, Robertas
Subathra, M. S. P.
George, S. Thomas
Mohammed, Mazin Abed
Sairamya, Nanjappan Jothiraj
AuthorAffiliation 1 Department of Electronics and Instrumentation Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; prasu1796@gmail.com (J.P.); sairamyanj@karunya.edu.in (N.J.S.)
4 Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
3 Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi 31000, Anbar, Iraq; mazinalshujeary@uoanbar.edu.iq
2 Department of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; subathra@karunya.edu
6 Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India
5 Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
AuthorAffiliation_xml – name: 1 Department of Electronics and Instrumentation Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; prasu1796@gmail.com (J.P.); sairamyanj@karunya.edu.in (N.J.S.)
– name: 6 Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India
– name: 3 Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi 31000, Anbar, Iraq; mazinalshujeary@uoanbar.edu.iq
– name: 5 Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
– name: 4 Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
– name: 2 Department of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; subathra@karunya.edu
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Cites_doi 10.7555/JBR.34.20190043
10.1109/BHI.2018.8333377
10.3390/e17020669
10.1016/j.clinph.2018.03.025
10.1016/j.bspc.2013.11.010
10.1080/21646821.2018.1428455
10.1016/j.clinph.2020.03.033
10.1186/s12911-016-0310-7
10.1109/TBME.2018.2850959
10.3390/s21206923
10.1109/CICC.2015.7338458
10.1109/ICSIPA.2015.7412252
10.1016/j.knosys.2016.11.023
10.1109/ACCESS.2020.2976866
10.1109/TBME.2012.2237399
10.1155/2015/986736
10.23919/ELINFOCOM.2018.8330671
10.1109/DATE.2012.6176511
10.1155/2014/450573
10.1109/ACCESS.2018.2810882
10.1109/JBHI.2017.2703873
10.1109/ACCESS.2017.2736014
10.1007/s10044-018-0691-6
10.1109/BHI.2018.8333388
10.1016/j.knosys.2015.08.004
10.1016/j.knosys.2013.02.014
10.1016/j.neucom.2018.10.108
10.1111/exsy.12211
10.1109/SAMI.2018.8323992
10.1007/978-3-319-13117-7_128
10.1016/j.bbe.2017.08.006
10.1109/TNSRE.2018.2850308
10.1109/TNSRE.2015.2505238
10.1109/78.157221
10.1016/j.eplepsyres.2017.11.015
10.1016/j.neunet.2018.04.018
10.1109/EMBC.2014.6944546
10.1016/j.eswa.2015.05.002
10.1007/s00034-017-0577-7
10.3390/s20174952
10.3390/pr9040682
10.1109/CEC.2017.7969588
10.1016/j.compbiomed.2016.02.016
10.1109/ICSEng.2017.56
10.1109/JBHI.2016.2553368
10.1109/ICICICT1.2017.8342653
10.1016/0013-4694(70)90143-4
10.1186/1687-6180-2014-183
10.1007/978-3-642-40728-4_26
10.1109/ISCAS.2018.8351687
10.1109/STA.2016.7952088
10.1109/TNSRE.2017.2697920
10.1016/j.bspc.2017.09.029
10.1109/5.241506
10.1016/j.seizure.2015.01.012
10.1007/s10994-015-5519-7
10.1109/MLSP.2016.7738825
10.1109/EMBC.2016.7590831
10.1016/j.clinph.2018.01.019
10.1109/ICACCCT.2014.7019312
10.1109/TNSRE.2016.2618937
10.1049/cp.2013.2060
10.1006/acha.1995.1019
10.1109/SPIS.2015.7422314
10.1016/j.bspc.2017.07.022
10.1109/ISCAS.2018.8351728
10.1016/j.jneumeth.2015.01.015
10.1016/j.eswa.2015.04.068
10.1109/MSPCT.2011.6150470
10.1016/j.cmpb.2017.03.002
10.1016/j.aci.2015.01.001
10.1007/s11517-017-1613-2
10.1007/s00034-015-0178-2
10.1109/ISPCC.2012.6224361
10.1016/j.compbiomed.2018.05.019
10.1007/978-981-10-6496-8_51
10.1007/978-3-030-66633-0
10.1109/ISOCC.2016.7799862
10.1016/j.neures.2013.06.002
10.1109/ICSPIS.2017.8311582
10.1016/j.cmpb.2016.04.014
10.1007/s13246-016-0520-4
10.1016/j.cmpb.2011.10.002
10.1016/j.jbi.2014.02.005
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References ref_94
ref_91
Kutlu (ref_89) 2011; 105
Pukenas (ref_51) 2018; 37
ref_13
Subathra (ref_95) 2020; 20
ref_10
ref_98
ref_97
Liang (ref_82) 2020; 396
ref_19
ref_18
ref_17
ref_16
ref_15
Scheffer (ref_5) 2018; 139
ref_25
ref_21
ref_20
Samiee (ref_55) 2017; 118
Ke (ref_72) 2018; 6
Ibrahim (ref_79) 2018; 38
ref_26
Ramakrishnan (ref_64) 2018; 22
Tsiouris (ref_70) 2018; 40
Acharya (ref_2) 2013; 45
ref_71
Cho (ref_52) 2017; 25
Harpale (ref_66) 2018; 33
ref_77
Learned (ref_88) 1993; 2
Bhattacharyya (ref_54) 2017; 64
ref_74
Xiang (ref_27) 2015; 243
ref_73
Ilakiyaselvan (ref_99) 2020; 34
Raghu (ref_83) 2020; 131
Vidyaratne (ref_57) 2017; 25
Zandi (ref_4) 2013; 60
Deng (ref_76) 2018; 26
ref_80
Fan (ref_75) 2018; 66
ref_86
Samiee (ref_29) 2015; 42
Janjarasjitt (ref_58) 2017; 40
Truong (ref_65) 2018; 105
Alickovic (ref_68) 2018; 39
Damasevicius (ref_100) 2014; 20
Martisius (ref_12) 2012; 119
Janjarasjitt (ref_48) 2017; 55
Wornell (ref_85) 1993; 81
Sharma (ref_93) 2015; 17
Menshawy (ref_28) 2015; 42
ref_56
Ke (ref_78) 2018; 459
Selvakumari (ref_67) 2018; 30
Khan (ref_62) 2017; 2
Trenite (ref_1) 2018; 129
Hjorth (ref_96) 1970; 29
Zabihi (ref_34) 2016; 24
Zhang (ref_43) 2016; 20
ref_59
Alotaiby (ref_90) 2014; 2014
Ahammad (ref_24) 2014; 2014
Chu (ref_50) 2017; 143
ref_61
ref_60
Tsiouris (ref_69) 2018; 99
Shoeb (ref_14) 2011; 22
Fisher (ref_7) 2018; 58
Ahmad (ref_53) 2017; 5
Sarmast (ref_6) 2020; 12
Artameeyanant (ref_3) 2017; 34
Smith (ref_30) 2016; 102
Usman (ref_81) 2020; 8
Faust (ref_87) 2015; 26
Behnam (ref_42) 2016; 132
Fergus (ref_40) 2016; 12
ref_36
ref_35
Khanmohammadi (ref_63) 2018; 22
ref_33
Murali (ref_31) 2016; 35
Acharya (ref_92) 2015; 88
Kiranyaz (ref_22) 2014; 49
ref_39
Orosco (ref_41) 2016; 71
ref_38
ref_37
Backhouse (ref_9) 2018; 129
Birvinskas (ref_11) 2012; 2012
ref_47
ref_46
ref_45
ref_44
Vetterli (ref_84) 1992; 40
Chen (ref_23) 2014; 10
ref_49
Fergus (ref_32) 2015; 2015
Noda (ref_8) 2013; 77
References_xml – volume: 20
  start-page: 55
  year: 2014
  ident: ref_100
  article-title: Fractional delay time embedding of EEG signals into high dimensional phase space
  publication-title: Electron. Electr. Eng.
– ident: ref_80
– volume: 34
  start-page: 240
  year: 2020
  ident: ref_99
  article-title: Deep learning approach to detect seizure using reconstructed phase space images
  publication-title: J. Biomed. Res.
  doi: 10.7555/JBR.34.20190043
– ident: ref_73
  doi: 10.1109/BHI.2018.8333377
– volume: 17
  start-page: 669
  year: 2015
  ident: ref_93
  article-title: Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals
  publication-title: Entropy
  doi: 10.3390/e17020669
– volume: 129
  start-page: 1770
  year: 2018
  ident: ref_9
  article-title: Characteristics of ictal EEG in magnetic seizure therapy at various stimulation frequencies
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2018.03.025
– ident: ref_16
– volume: 10
  start-page: 1
  year: 2014
  ident: ref_23
  article-title: A framework on wavelet-based non-linear features and extreme learning machine for epileptic seizure detection
  publication-title: Biomed. Signal Process. Control
  doi: 10.1016/j.bspc.2013.11.010
– volume: 58
  start-page: 1
  year: 2018
  ident: ref_7
  article-title: The revised definition and classification of epilepsy for neurodiagnostic technologists
  publication-title: Neurodiagn. J.
  doi: 10.1080/21646821.2018.1428455
– volume: 131
  start-page: 1567
  year: 2020
  ident: ref_83
  article-title: Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2020.03.033
– ident: ref_39
  doi: 10.1186/s12911-016-0310-7
– volume: 66
  start-page: 601
  year: 2018
  ident: ref_75
  article-title: Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2018.2850959
– ident: ref_10
  doi: 10.3390/s21206923
– ident: ref_37
  doi: 10.1109/CICC.2015.7338458
– volume: 22
  start-page: 1443
  year: 2011
  ident: ref_14
  article-title: An algorithm for detecting seizure termination in scalp EEG
  publication-title: Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS
– ident: ref_35
  doi: 10.1109/ICSIPA.2015.7412252
– volume: 118
  start-page: 228
  year: 2017
  ident: ref_55
  article-title: Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2016.11.023
– volume: 8
  start-page: 39998
  year: 2020
  ident: ref_81
  article-title: Epileptic seizures prediction using deep learning techniques
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.2976866
– volume: 60
  start-page: 1401
  year: 2013
  ident: ref_4
  article-title: Predicting Epileptic Seizures in Scalp EEG Based on a Variational Bayesian Gaussian Mixture Model of Zero-Crossing Intervals
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2012.2237399
– volume: 2015
  start-page: 1
  year: 2015
  ident: ref_32
  article-title: Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
  publication-title: Biomed. Res. Int.
  doi: 10.1155/2015/986736
– ident: ref_71
  doi: 10.23919/ELINFOCOM.2018.8330671
– ident: ref_18
  doi: 10.1109/DATE.2012.6176511
– volume: 2014
  start-page: 450573
  year: 2014
  ident: ref_24
  article-title: Detection of Epileptic Seizure Event and Onset Using EEG, Detection of Epileptic Seizure Event and Onset Using EEG
  publication-title: Biomed. Res. Int.
  doi: 10.1155/2014/450573
– volume: 6
  start-page: 14722
  year: 2018
  ident: ref_72
  article-title: Towards Brain Big Data Classification: Epileptic EEG Identification with a Lightweight VGGNet on Global MIC
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2810882
– volume: 22
  start-page: 154
  year: 2018
  ident: ref_63
  article-title: Adaptive Seizure Onset Detection Framework Using a Hybrid PCA—CSP Approach
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2017.2703873
– volume: 5
  start-page: 16919
  year: 2017
  ident: ref_53
  article-title: Mallat’s Scattering Transform Based Anomaly Sensing for Detection of Seizures in Scalp EEG
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2017.2736014
– volume: 22
  start-page: 1161
  year: 2018
  ident: ref_64
  article-title: Epileptic seizure detection using fuzzy-rules-based sub-band specific features and layered multi-class SVM
  publication-title: Pattern Anal. Appl.
  doi: 10.1007/s10044-018-0691-6
– ident: ref_91
  doi: 10.1109/BHI.2018.8333388
– ident: ref_86
– volume: 88
  start-page: 85
  year: 2015
  ident: ref_92
  article-title: Knowledge-Based Systems Application of entropies for automated diagnosis of epilepsy using EEG signals: A review
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2015.08.004
– ident: ref_44
– volume: 45
  start-page: 147
  year: 2013
  ident: ref_2
  article-title: Knowledge-Based Systems Automated EEG analysis of epilepsy: A review
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2013.02.014
– volume: 396
  start-page: 569
  year: 2020
  ident: ref_82
  article-title: Scalp EEG epileptogenic zone recognition and localization based on a long-term recurrent convolutional network
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2018.10.108
– volume: 34
  start-page: 1
  year: 2017
  ident: ref_3
  article-title: Electroencephalography-based feature extraction using the complex network for automated epileptic seizure detection
  publication-title: Expert. Syst.
  doi: 10.1111/exsy.12211
– ident: ref_97
  doi: 10.1109/SAMI.2018.8323992
– volume: 2
  start-page: 7
  year: 2017
  ident: ref_62
  article-title: A Low Complexity Patient-Specific threshold-based Accelerator for the Grand-Mal Seizure Disorder
  publication-title: Biomed. Circuits Syst. (BioCAS)
– ident: ref_38
  doi: 10.1007/978-3-319-13117-7_128
– volume: 38
  start-page: 16
  year: 2018
  ident: ref_79
  article-title: Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis
  publication-title: Biocybern. Biomed. Eng.
  doi: 10.1016/j.bbe.2017.08.006
– volume: 26
  start-page: 1481
  year: 2018
  ident: ref_76
  article-title: Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2018.2850308
– volume: 33
  start-page: 1
  year: 2018
  ident: ref_66
  article-title: An adaptive method for feature selection and extraction for the classification of epileptic EEG signal insignificant states
  publication-title: J. King Saud Univ.—Comput. Inf. Sci.
– ident: ref_36
– volume: 24
  start-page: 386
  year: 2016
  ident: ref_34
  article-title: Analysis of High-Dimensional Phase Space via Poincaré Section for Patient-Specific Seizure Detection
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2015.2505238
– volume: 40
  start-page: 2207
  year: 1992
  ident: ref_84
  article-title: Wavelets and Filter Banks: Theory and Design
  publication-title: IEEE Trans. Signal Process.
  doi: 10.1109/78.157221
– volume: 139
  start-page: 73
  year: 2018
  ident: ref_5
  article-title: The new definition and classification of seizures and epilepsy
  publication-title: Epilepsy Res.
  doi: 10.1016/j.eplepsyres.2017.11.015
– volume: 105
  start-page: 104
  year: 2018
  ident: ref_65
  article-title: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram
  publication-title: Neural Netw.
  doi: 10.1016/j.neunet.2018.04.018
– ident: ref_25
  doi: 10.1109/EMBC.2014.6944546
– volume: 42
  start-page: 7175
  year: 2015
  ident: ref_29
  article-title: Long-term epileptic EEG classification via 2D mapping and textural features
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2015.05.002
– volume: 37
  start-page: 873
  year: 2018
  ident: ref_51
  article-title: Algorithm for the Detection of Changes in the Dynamics of a Multivariate Time Series via Sliced Cross-Bispectrum. Circuits
  publication-title: Syst. Signal Process.
  doi: 10.1007/s00034-017-0577-7
– volume: 20
  start-page: 4952
  year: 2020
  ident: ref_95
  article-title: Detection of focal and non-focal electroencephalogram signals using fast Walsh-Hadamard transform and artificial neural network
  publication-title: Sensors
  doi: 10.3390/s20174952
– ident: ref_98
  doi: 10.3390/pr9040682
– ident: ref_56
  doi: 10.1109/CEC.2017.7969588
– volume: 71
  start-page: 128
  year: 2016
  ident: ref_41
  article-title: Patient non-specific algorithm for seizures detection in scalp EEG
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2016.02.016
– volume: 2012
  start-page: 199
  year: 2012
  ident: ref_11
  article-title: EEG dataset reduction and feature extraction using discrete cosine transform. UKSim-AMSS 6th European Modelling Symposium
  publication-title: EMS
– ident: ref_61
  doi: 10.1109/ICSEng.2017.56
– volume: 20
  start-page: 996
  year: 2016
  ident: ref_43
  article-title: Design and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2016.2553368
– ident: ref_60
  doi: 10.1109/ICICICT1.2017.8342653
– volume: 29
  start-page: 306
  year: 1970
  ident: ref_96
  article-title: EEG analysis based on time domain properties
  publication-title: Electroencephalogr. Clin. Neurophysiol.
  doi: 10.1016/0013-4694(70)90143-4
– volume: 119
  start-page: 99
  year: 2012
  ident: ref_12
  article-title: Using higher-order nonlinear operators for SVM classification of EEG data
  publication-title: Electron. Electr. Eng.
– volume: 2014
  start-page: 183
  year: 2014
  ident: ref_90
  article-title: EEG seizure detection and prediction algorithms: A survey
  publication-title: EURASIP J. Adv. Signal Process.
  doi: 10.1186/1687-6180-2014-183
– ident: ref_13
  doi: 10.1007/978-3-642-40728-4_26
– ident: ref_74
  doi: 10.1109/ISCAS.2018.8351687
– ident: ref_49
  doi: 10.1109/STA.2016.7952088
– volume: 25
  start-page: 2146
  year: 2017
  ident: ref_57
  article-title: Real-Time Epileptic Seizure Detection Using EEG
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2017.2697920
– volume: 40
  start-page: 275
  year: 2018
  ident: ref_70
  article-title: A robust unsupervised epileptic seizure detection methodology to accelerate large EEG database evaluation
  publication-title: Biomed. Signal Process. Control
  doi: 10.1016/j.bspc.2017.09.029
– volume: 81
  start-page: 1428
  year: 1993
  ident: ref_85
  article-title: Wavelet-Based Representations for the l/f Family of Fractal Processes
  publication-title: Proc. IEEE
  doi: 10.1109/5.241506
– volume: 26
  start-page: 56
  year: 2015
  ident: ref_87
  article-title: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
  publication-title: Seizure Eur. J. Epilepsy
  doi: 10.1016/j.seizure.2015.01.012
– volume: 102
  start-page: 309
  year: 2016
  ident: ref_30
  article-title: Multi-task seizure detection: Addressing intra-patient variation in seizure morphologies
  publication-title: Mach. Learn.
  doi: 10.1007/s10994-015-5519-7
– ident: ref_45
  doi: 10.1109/MLSP.2016.7738825
– volume: 64
  start-page: 2003
  year: 2017
  ident: ref_54
  article-title: A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform. IEEE Trans
  publication-title: Biomed. Eng.
– ident: ref_46
  doi: 10.1109/EMBC.2016.7590831
– ident: ref_17
– volume: 129
  start-page: 1056
  year: 2018
  ident: ref_1
  article-title: Clinical Utility of EEG in Diagnosing and Monitoring Epilepsy in Adults
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2018.01.019
– ident: ref_26
  doi: 10.1109/ICACCCT.2014.7019312
– ident: ref_20
– volume: 25
  start-page: 1309
  year: 2017
  ident: ref_52
  article-title: EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2016.2618937
– ident: ref_21
  doi: 10.1049/cp.2013.2060
– volume: 2
  start-page: 265
  year: 1993
  ident: ref_88
  article-title: A Wavelet Packet Approach to Transient Signal Classification
  publication-title: Appl. Comput. Harmon. Anal.
  doi: 10.1006/acha.1995.1019
– ident: ref_33
  doi: 10.1109/SPIS.2015.7422314
– volume: 39
  start-page: 94
  year: 2018
  ident: ref_68
  article-title: Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction
  publication-title: Biomed. Signal Process. Control
  doi: 10.1016/j.bspc.2017.07.022
– ident: ref_77
  doi: 10.1109/ISCAS.2018.8351728
– volume: 243
  start-page: 18
  year: 2015
  ident: ref_27
  article-title: The detection of epileptic seizure signals based on fuzzy entropy
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2015.01.015
– volume: 42
  start-page: 7157
  year: 2015
  ident: ref_28
  article-title: An automatic mobile-health based approach for EEG epileptic seizures detection
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2015.04.068
– ident: ref_15
  doi: 10.1109/MSPCT.2011.6150470
– volume: 143
  start-page: 75
  year: 2017
  ident: ref_50
  article-title: Predicting epileptic seizures from scalp EEG based on attractor state analysis
  publication-title: Comput. Methods Programs Biomed.
  doi: 10.1016/j.cmpb.2017.03.002
– volume: 12
  start-page: 70
  year: 2016
  ident: ref_40
  article-title: A machine learning system for automated whole-brain seizure detection
  publication-title: Appl. Comput. Inform.
  doi: 10.1016/j.aci.2015.01.001
– volume: 55
  start-page: 1743
  year: 2017
  ident: ref_48
  article-title: Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM
  publication-title: Med. Biol. Eng. Comput.
  doi: 10.1007/s11517-017-1613-2
– volume: 35
  start-page: 2914
  year: 2016
  ident: ref_31
  article-title: An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal. Circuits
  publication-title: Syst. Signal Process.
  doi: 10.1007/s00034-015-0178-2
– volume: 12
  start-page: e10549
  year: 2020
  ident: ref_6
  article-title: Current classification of seizures and epilepsies: Scope, limitations and recommendations for future action
  publication-title: Cureus
– ident: ref_19
  doi: 10.1109/ISPCC.2012.6224361
– volume: 99
  start-page: 24
  year: 2018
  ident: ref_69
  article-title: A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2018.05.019
– volume: 459
  start-page: 553
  year: 2018
  ident: ref_78
  article-title: A shallow-dense network approach to synchronization pattern classification of multivariate epileptic EEG
  publication-title: Lect. Notes Electr. Eng.
  doi: 10.1007/978-981-10-6496-8_51
– ident: ref_94
  doi: 10.1007/978-3-030-66633-0
– ident: ref_47
  doi: 10.1109/ISOCC.2016.7799862
– volume: 77
  start-page: 70
  year: 2013
  ident: ref_8
  article-title: Potentiation of quantitative electroencephalograms following prefrontal repetitive transcranial magnetic stimulation in patients with major depression
  publication-title: Neurosci. Res.
  doi: 10.1016/j.neures.2013.06.002
– ident: ref_59
  doi: 10.1109/ICSPIS.2017.8311582
– volume: 132
  start-page: 115
  year: 2016
  ident: ref_42
  article-title: Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search
  publication-title: Comput. Methods Programs Biomed.
  doi: 10.1016/j.cmpb.2016.04.014
– volume: 40
  start-page: 57
  year: 2017
  ident: ref_58
  article-title: Performance of epileptic single-channel scalp EEG classifications using single wavelet-based features
  publication-title: Australas. Phys. Eng. Sci. Med.
  doi: 10.1007/s13246-016-0520-4
– volume: 105
  start-page: 257
  year: 2011
  ident: ref_89
  article-title: Feature extraction for ECG heartbeats using higher-order statistics of WPD coefficients
  publication-title: Comput. Methods Programs Biomed.
  doi: 10.1016/j.cmpb.2011.10.002
– volume: 30
  start-page: 1
  year: 2018
  ident: ref_67
  article-title: Epileptic seizure detection by analyzing high dimensional phase space via Poincaré section
  publication-title: Multidimens. Syst. Signal Process.
– volume: 49
  start-page: 16
  year: 2014
  ident: ref_22
  article-title: Automated patient-specific classification of long-term Electroencephalography
  publication-title: J. Biomed. Inform.
  doi: 10.1016/j.jbi.2014.02.005
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Snippet Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists...
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SubjectTerms Accuracy
Automation
Classification
Convulsions & seizures
Diagnosis
Discriminant analysis
EEG
Electroencephalography
Epilepsy
Feature selection
Machine learning
Patients
Pediatrics
Precision medicine
Review
Seizures
Signal processing
Support vector machines
Wavelet transforms
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Title Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey
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