| [1] LEE S, YEOM J S.Revised Classification and Definition of Epilepsy Syndromes with Onset in Childhood by the International League Against Epilepsy Task Force. Epilia: Epilepsy and Community, 2023, 5(1): 28-39.
[2] ZUBERI S M, WIRRELL E, YOZAWITZ E, et al. ILAE Classification and Definition of Epilepsy Syndromes with Onset in Neonates and Infants: Position Statement by the ILAE Task Force on Nosology and Definitions. Epilepsia, 2022, 63(6): 1349-1397.
[3] HARRIS L, ANGUS-LEPPAN H.Epilepsy Update: Diagnosis, Cla-ssification and Management. Medicine, 2023, 51(8): 545-551.
[4] 张瑞,李亚红,李荣,等.癫痫患儿疾病家庭负担的现状及影响因素研究.神经疾病与精神卫生, 2023, 23(7): 483-489.
(ZHANG R, LI Y H, LI R, et al. Current Status and Influencing Factors of Family Burden of Epilepsy Children. Journal of Neuroscience and Mental Health, 2023, 23(7): 483-489.)
[5] The Lancet Neurology.Epilepsy Syndromes: An Essential Piece of the Puzzle. The Lancet Neurology, 2022, 21(7): 577.
[6] WIRRELL E C, RINEY K, SPECCHIO N, et al. How Have the Recent Updated Epilepsy Classifications Impacted on Diagnosis and Treatment? Expert Review of Neurotherapeutics, 2023, 23(11): 969-980.
[7] KOUTROUMANIDIS M, ARZIMANOGLOU A, CARABALLO R, et al. The Role of EEG in the Diagnosis and Classification of the Epilepsy Syndromes: A Tool for Clinical Practice by the ILAE Neurophysiology Task Force(Part 1). Epileptic Disorders, 2017, 19(3): 233-298.
[8] NOACHTAR S. RMI J.The Role of EEG in Epilepsy: A Critical Review. Epilepsy and Behavior, 2009, 15(1): 22-33.
[9] SMITH S J M. EEG in the Diagnosis, Classification, and Management of Patients with Epilepsy. Journal of Neurology, Neurosurgery and Psychiatry, 2005, 76(S2): ii2-ii7.
[10] FISHER R S, VAN EMDE BOAS W, BLUME W, et al. Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy(ILAE) and the International Bureau for Epilepsy(IBE). Epilepsia, 2005, 46(4): 470-472.
[11] PRESSLER R M, LAGAE L.Why We Urgently Need Improved Seizure and Epilepsy Therapies for Children and Neonates. Neuropharmacology, 2020, 170. DOI: 10.1016/j.neuropharm.2019.107854.
[12] TRINKA E, KWAN P, LEE B, et al. Epilepsy in Asia: Disease Burden, Management Barriers, and Challenges. Epilepsia, 2019, 60(S1): 7-21.
[13] MILÀ B R, SINDHU K R, MYTINGER J R, et al. EEG Biomar-kers for the Diagnosis and Treatment of Infantile Spasms. Frontiers in Neurology, 2022, 13. DOI: 10.3389/fneur.2022.960454.
[14] LHATOO S D, BERNASCONI N, BLUMCKE I, et al. Big Data in Epilepsy: Clinical and Research Considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy. Epilepsia, 2020, 61(9): 1869-1883.
[15] 李莹,欧阳楷.自动检测儿童脑电中癫痫波的方法研究.中国生物医学工程学报, 2005, 24(5): 541-545.
(LI Y, OUYANG K.Study of Automatic Detection of Epileptiform Waves in Children EEG. Chinese Journal of Biomedical Enginee-ring, 2005, 24(5): 541-545.)
[16] SUERI C, GASPARINI S, BALESTRINI S, et al. Diagnostic Biomarkers of Epilepsy. Current Pharmaceutical Biotechnology, 2018, 19(6): 440-450.
[17] JACOBS J, LEVAN P, CHANDER R, et al. Interictal High-Frequency Oscillations(80-500 Hz) Are An Indicator of Seizure Onset Areas Independent of Spikes in the Human Epileptic Brain. Epilepsia, 2008, 49(11): 1893-1907.
[18] GAO Z K, DANG W D, WANG X M, et al. Complex Networks and Deep Learning for EEG Signal Analysis. Cognitive Neurodynamics, 2021, 15(3): 369-388.
[19] AHMED M I B, ALOTAIBI S, DASH S, et al. A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy. SN Computer Science, 2022, 3(6). DOI: 10.1007/s42979-022-01358-9.
[20] RASHEED K, QAYYUM A, QADIR J, et al. Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review. IEEE Reviews in Biomedical Engineering, 2021, 14: 139-155.
[21] FAROOQ M S, ZULFIQAR A, RIAZ S.Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Cha-llenges. Diagnostics, 2023, 13(6). DOI: 10.3390/diagnostics13061058.
[22] HOSSEINI M P, HOSSEINI A, AHI K.A Review on Machine Lear-ning for EEG Signal Processing in Bioengineering. IEEE Reviews in Biomedical Engineering, 2020, 14: 204-218.
[23] NHU D, JANMOHAMED M, ANTONIC-BAKER A, et al. Deep Learning for Automated Epileptiform Discharge Detection from Scalp EEG: A Systematic Review. Journal of Neural Engineering, 2022, 19(5). DOI: 10.1088/1741-2552/ac9644.
[24] SHOEIBI A, GHASSEMI N, KHODATARS M, et al. Applications of Epileptic Seizures Detection in Neuroimaging Modalities Using Deep Learning Techniques: Methods, Challenges, and Future Works[C/OL].[2023-10-12]. https://arxiv.org/abs/2105.14278v1.
[25] AN S, KANG C, LEE H W.Artificial Intelligence and Computational Approaches for Epilepsy. Journal of Epilepsy Research, 2020, 10(1): 8-17.
[26] GOMBOLAY G Y, GOPALAN N, BERNASCONI A, et al. Review of Machine Learning and Artificial Intelligence(ML/AI) for the Pediatric Neurologist. Pediatric Neurology, 2023, 141: 42-51.
[27] ACHARYA U R, SREE S V, SWAPNA G, et al. Automated EEG Analysis of Epilepsy: A Review. Knowledge-Based Systems, 2013, 45: 147-165.
[28] 彭睿旻,江军,匡光涛,等.基于EEG的癫痫自动检测:综述与展望.自动化学报, 2022, 48(2): 335-350.
(PENG R M, JIANG J, KUANG G T, et al. EEG-Based Automatic Epilepsy Detection: Review and Outlook. Acta Automatica Sinica, 2022, 48(2): 335-350.)
[29] KUHLMANN L, KAROLY P, FREESTONE D R, et al. Epilep-syecosystem.org: Crowd-Sourcing Reproducible Seizure Prediction with Long-Term Human Intracranial EEG. Brain, 2018, 141(9): 2619-2630.
[30] MEISEL C, LODDENKEMPER T.Seizure Prediction and Intervention. Neuropharmacology, 2020, 172. DOI: 10.1016/j.neuropharm.2019.107898.
[31] GLAUSER T, BEN-MENACHEM E, BOURGEOIS B, et al. Updated ILAE Evidence Review of Antiepileptic Drug Efficacy and Effectiveness as Initial Monotherapy for Epileptic Seizures and Syndromes. Epilepsia, 2013, 54(3): 551-563.
[32] FISHER R S, CROSS J H, FRENCH J A, et al. Operational Cla-ssification of seizure Types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia, 2017, 58(4): 522-530.
[33] ANDRADE-MACHADO R, CUARTAS V B, MUHAMMAD I K.Recognition of Interictal and Ictal Discharges on EEG. Focal vs Generalized Epilepsy. Epilepsy and Behavior, 2021, 117. DOI:10.1016/j.yebeh.2021.107830.
[34] PRESSLER R M, CILIO M R, MIZRAHI E M, et al. The ILAE Classification of Seizures and the Epilepsies: Modification for Seizures in the Neonate. Position Paper by the ILAE Task Force on Neonatal Seizures. Epilepsia, 2021, 62(3): 615-628.
[35] SPECCHIO N, WIRRELL E C, SCHEFFER I E, et al. International League Against Epilepsy Classification and Definition of Epilepsy Syndromes with Onset in Childhood: Position Paper by the ILAE Task Force on Nosology and Definitions. Epilepsia, 2022, 63(6): 1398-1442.
[36] RINEY K, BOGACZ A, SOMERVILLE E, et al. International Lea-gue Against Epilepsy Classification and Definition of Epilepsy Syndromes with Onset at a Variable Age: Position Statement by the ILAE Task Force on Nosology and Definitions. Epilepsia, 2022, 63(6): 1443-1474.
[37] TATUM W O, MANI J, JIN K, et al. Minimum Standards for Inpatient Long-Term Video-EEG Monitoring: A Clinical Practice Gui-deline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology. Clinical Neurophysiology, 2022, 134: 111-128.
[38] FISHER R S, ACEVEDO C, ARZIMANOGLOU A, et al. ILAE Official Report: A Practical Clinical Definition of Epilepsy. Epilepsia, 2014, 55(4): 475-482.
[39] KATYAYAN A, DIAZ-MEDINA G.Epilepsy: Epileptic Syndromes and Treatment. Neurologic Clinics, 2021, 39(3): 779-795.
[40] GUERRINI R.Epilepsy in Children. The Lancet, 2006, 367(9509): 499-524.
[41] KNIGHT E M P, WYLLIE E. West Syndrome and the New Classification of Epilepsy. The Lancet Neurology, 2022, 21(8): 689.
[42] FEJERMAN N, CARABALLO R H, DALLA BERNARDINA B. Benign Childhood Epilepsy with Centrotemporal Spikes // IUPANC M L, ed. Benign Focal Epilepsies in Infancy, Childhood and Adolescence. Paris, France: John Libbey Eurotext, 2007: 77-113.
[43] GROSSO S, GALIMBERTI D, VEZZOSI P, et al. Childhood Absence Epilepsy: Evolution and Prognostic Factors. Epilepsia, 2005, 46(11): 1796-1801.
[44] RUBBOLI G, GARDELLA E, CANTALUPO G, et al. Encephalopathy Related to Status Epilepticus During Slow Sleep(ESES). Pathophysiological Insights and Nosological Considerations. Epilepsy and Behavior, 2023, 140. DOI: 10.1016/j.yebeh.2023.109105.
[45] NAKASATO N, LEVESQUE M F, BARTH D S, et al. Comparisons of MEG, EEG, and ECoG Source Localization in Neocortical Partial Epilepsy in Humans. Electroencephalography and Clinical Neurophysiology, 1994, 91(3): 171-178.
[46] KOVAC S, VAKHARIA V N, SCOTT C, et al. Invasive Epilepsy Surgery Evaluation. Seizure, 2017, 44: 125-136.
[47] BARKLEY G L, BAUMGARTNER C.MEG and EEG in Epile-psy. Journal of Clinical Neurophysiology, 2003, 20(3): 163-178.
[48] DETRE J A. fMRI: Applications in Epilepsy. Epilepsia, 2004, 45(S4): 26-31.
[49] PILLAI J, SPERLING M R.Interictal EEG and the Diagnosis of Epilepsy. Epilepsia, 2006, 47(S1): 14-22.
[50] FLINK R, PEDERSEN B, GUEKHT A B, et al. Guidelines for the Use of EEG Methodology in the Diagnosis of Epilepsy: International League Against Epilepsy: Commission Report Commission on European Affairs: Subcommission on European Guidelines. Acta Neurologica Scandinavica, 2002, 106(1): 1-7.
[51] YOSHINAGA H, HATTORI J, OHTA H, et al. Utility of the Scalp-Recorded Ictal EEG in Childhood Epilepsy. Epilepsia, 2001, 42(6): 772-777.
[52] KLEM G H, IÜDERS H O. Ten-Twenty Electrode System of the International Federation. Electroencephalogr and Clinical Neurophysiology, 1999, 52: 3-6.
[53] NARIAI H, HUSSAIN S A, BERNARDO D, et al. Scalp EEG Interictal High Frequency Oscillations as an Objective Biomarker of Infantile Spasms. Clinical Neurophysiology, 2020, 131(11): 2527-2536.
[54] ANDRZEJAK R G, LEHNERTZ K, MORMANN F, et al. Indications of Nonlinear Deterministic and Finite-Dimensional Structures in Time Series of Brain Electrical Activity: Dependence on Recor-ding Region and Brain State. Physical Review E, 2001, 64(6). DOI: 10.1103/PhysRevE.64.061907.
[55] GOLDBERGER A L, AMARAL L A, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 2000, 101(23): e215-e220.
[56] SWAMI P, PANIGRAHI B, NARA S, et al. EEG Epilepsy Datasets[DB/OL].[2023-10-12]. https://DOI.org/10.13140/RG.2.2.14280.32006.
[57] SHAH V, VON WELTIN E, LOPEZ S,et al. The Temple University Hospital Seizure Detection Corpus. Frontiers in Neuroinforma-tics, 2018, 12. DOI: 10.3389/fninf.2018.00083.
[58] DETTI P, VATTI G, ZABALO MANRIQUE DE LARA G. EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings. Processes, 2020, 8(7). DOI: 10.3390/pr8070846.
[59] STAFSTROM C E, CARMANT L.Seizures and Epilepsy: An Overview for Neuroscientists. Cold Spring Harbor Perspectives in Medicine, 2015, 5(6). DOI: 10.1101/cshperspect.a022426.
[60] AMIN U, NASCIMENTO F A, KARAKIS I, et al. Normal Variants and Artifacts: Importance in EEG Interpretation. Epileptic Disorders, 2023, 25(5): 591-648.
[61] DALY I, NICOLAOU N, NASUTO S J, et al. Automated Artifact Removal from the Electroencephalogram: A Comparative Study. Clinical EEG and Neuroscience, 2013, 44(4): 291-306.
[62] ISLAM M K, RASTEGARNIA A, YANG Z.Methods for Artifact Detection and Removal from Scalp EEG: A Review. Neurophysio-logie Clinique/Clinical Neurophysiology, 2016, 46(4/5): 287-305.
[63] MUMTAZ W, RASHEED S, IRFAN A.Review of Challenges Asso-ciated with the EEG Artifact Removal Methods. Biomedical Signal Processing and Control, 2021, 68. DOI: 10.1016/j.bspc.2021.102741.
[64] DE VOS M, DEBURCHGRAEVE W, CHERIAN P J, et al. Automated Artifact Removal as Preprocessing Refines Neonatal Seizure Detection. Clinical Neurophysiology, 2011,122(12): 2345-2354.
[65] PIPER D, STRUNGARU R, WITTE H.Artefact Removal Approa-ch for Epileptic EEG Data. UPB Scientific Bulletin(Series C), 2015, 77(4): 213-222.
[66] JIANG X, BIAN G B, TIAN Z.Removal of Artifacts from EEG Signals: A Review. Sensors, 2019, 19(5). DOI: 10.3390/s19050987.
[67] PELTOLA M E, LEITINGER M, HALFORD J J, et al. Routine and Sleep EEG: Minimum Recording Standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy. Clinical Neurophysiology, 2023, 147: 108-120.
[68] ZHENG R Z, FENG Y M, WANG T L, et al. Scalp EEG Functional Connection and Brain Network in Infants with West Syndrome. Neural Networks, 2022, 153: 76-86.
[69] FANG Z B, HU D H, ZHENG R Z, et al. Multiple Artifact Detection Based on Adaptive Scalp Region Selection and Classifier Fusion. IEEE Sensors Journal, 2024. DOI: 10.1109/JSEN.2024.3358911.
[70] ABD RAHMAN F, OTHMAN M F, SHAHARUDDIN N A.Analysis Methods of EEG Signals: A Review in EEG Application for Brain Disorder. Jurnal Teknologi(Sciences and Engineering), 2015, 72(2). DOI: 10.11113/jt.v72.3886.
[71] NEVADO-HOLGADO A J, MARTEN F, RICHARDSON M P, et al. Characterising the Dynamics of EEG Waveforms as the Path through Parameter Space of a Neural Mass Model: Application to Epilepsy Seizure Evolution. NeuroImage, 2012, 59(3): 2374-2392.
[72] OUYANG C S, CHIANG C T, YANG R C, et al. Quantitative EEG Findings and Response to Treatment with Antiepileptic Medications in Children with Epilepsy. Brain and Development, 2018, 40(1): 26-35.
[73] ACHARYA U R, SREE S V, CHATTOPADHYAY S, et al. App-lication of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals. International Journal of Neural Systems, 2011, 21(3): 199-211.
[74] VAN KLINK N, FRAUSCHER B, ZIJLMANS M, et al. Relationships between Interictal Epileptic Spikes and Ripples in Surface EEG. Clinical Neurophysiology, 2016, 127(1): 143-149.
[75] KHAN Y U, FAROOQ O, SHARMA P.Automatic Detection of Seizure Onset in Pediatric EEG. International Journal of Embedded Systems and Applications, 2012, 2(3): 81-89.
[76] XIANG J, MAUE E, FAN Y Y, et al. Kurtosis and Skewness of High-Frequency Brain Signals Are Altered in Pediatric Epilepsy. Brain Communications, 2020, 2(1). DOI: 10.1093/braincomms/fcaa036.
[77] SIKDAR D, ROY R, MAHADEVAPPA M.Epilepsy and Seizure Characterisation by Multifractal Analysis of EEG Subbands. Biomedical Signal Processing and Control, 2018, 41: 264-270.
[78] QUINTERO-RINCON A, PEREYRA M, D'GIANO C, et al. Fast Statistical Model-Based Classification of Epileptic EEG Signals. Biocybernetics and Biomedical Engineering, 2018, 38(4): 877-889.
[79] HU W B, CAO J W, LAI X P, et al. Mean Amplitude Spectrum Based Epileptic State Classification for Seizure Prediction Using Convolutional Neural Networks. Journal of Ambient Intelligence and Humanized Computing, 2019, 14: 15485-15495.
[80] HU D H, CAO J W, LAI X P, et al. Epileptic State Classification Based on Intrinsic Mode Function and Wavelet Packet Decomposition // Proc of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Washington, USA: IEEE, 2019: 2382-2385.
[81] ALICKOVIC E, KEVRIC J, SUBASI A.Performance Evaluation of Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packed Decomposition for Automated Epileptic Seizure Detection and Prediction. Biomedical Signal Processing and Control, 2018, 39: 94-102.
[82] LIU S, WANG J, LI S S,et al. Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization. IEEE Transactions on Neural Systems and Rehabilitation Enginee-ring, 2023, 31: 3884-3894.
[83] SAMIEE K, KOVÁCS P, GABBOUJ M. Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform. IEEE Transactions on Biomedical Engineering, 2015, 62(2): 541-552.
[84] ADELI H, ZHOU Z Q, DADMEHR N.Analysis of EEG Records in an Epileptic Patient Using Wavelet Transform. Journal of Neuroscience Methods, 2003, 123(1): 69-87.
[85] KIYMIK M K, GULER I, DIZIBUYUK A, et al. Comparison of STFT and Wavelet Transform Methods in Determining Epileptic Seizure Activity in EEG Signals for Real-Time Application. Computers in Biology and Medicine, 2005, 35(7): 603-616.
[86] GUO S Y, ZHANG F. A SPCNN Model for Patient-Independent Prediction of Epilepsy Using MFCC Features // Proc of the 12th International Conference on Information Science and Technology. Washington, USA: IEEE, 2022: 68-73.
[87] DISSANAYAKE T, FERNANDO T, DENMAN S, et al. Deep Lear-ning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals. IEEE Sensors Journal, 2021, 21(7): 9377-9388.
[88] LEE K, JEONG H, KIM S, et al. Real-Time Seizure Detection Using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting[C/OL].[2023-10-12]. https://arxiv.org/pdf/2201.08780.pdf.
[89] RODRIGUEZ-BERMUDEZ G, GARCIA-LAENCINA P J. Analysis of EEG Signals Using Nonlinear Dynamics and Chaos: A Review. Applied Mathematics and Information Sciences, 2015, 9(5): 2309-2321.
[90] WILLIAMSON J R, BLISS D W, BROWNE D W, et al. Seizure Prediction Using EEG Spatiotemporal Correlation Structure. Epilepsy and Behavior, 2012, 25(2): 230-238.
[91] SARGOLZAEI S, CABRERIZO M, GORYAWALA M, et al. Scalp EEG Brain Functional Connectivity Networks in Pediatric Epilepsy. Computers in Biology and Medicine, 2015, 56: 158-166.
[92] VAN DIESSEN E, OTTE W M, STAM C J, et al. Electroencephalography Based Functional Networks in Newly Diagnosed Childhood Epilepsies. Clinical Neurophysiology, 2016, 127(6): 2325-2332.
[93] CUI X N, CAO J W, HU D H, et al. Regional Scalp EEGs Analysis and Classification on Typical Childhood Epilepsy Syndromes. IEEE Transactions on Cognitive and Developmental Systems, 2023, 15(2): 662-674.
[94] BRUZZO A A, GESIERICH B, SANTI M, et al. Permutation Entropy to Detect Vigilance Changes and Preictal States from Scalp EEG in Epileptic Patients. A Preliminary Study. Neurological Sciences, 2008, 29(1): 3-9.
[95] WU D P, LI J, DONG F, et al. Classification of Seizure Types Based on Multi-class Specific Bands Common Spatial Pattern and Penalized Ensemble Model. Biomedical Signal Processing and Control, 2023, 79. DOI: 10.1016/j.bspc.2022.104118.
[96] HE F, BILLINGS S A, WEI H L,et al. A Nonlinear Causality Measure in the Frequency Domain: Nonlinear Partial Directed Coherence with Applications to EEG. Journal of Neuroscience Me-thods, 2014, 225: 71-80.
[97] YU H T, ZHU L, CAI L H, et al. Variation of Functional Brain Connectivity in Epileptic Seizures: An EEG Analysis with Cross-Frequency Phase Synchronization. Cognitive Neurodynamics, 2020, 14(1): 35-49.
[98] MA J Y, WANG Z Y, CHENG T Y, et al. A Prediction Model Integrating Synchronization Biomarkers and Clinical Features to Identify Responders to Vagus Nerve Stimulation Among Pediatric Patients with Drug-Resistant Epilepsy. CNS Neuroscience and Therapeutics, 2022, 28(11): 1838-1848.
[99] DAI X J, XU Q, HU J P, et al. BECTS Substate Classification by Granger Causality Density Based Support Vector Machine Model. Frontiers in Neurology, 2019,10. DOI: 10.3389/fneur.2019.01201.
[100] HU D H, CAO J W, LAI X P, et al. Epileptic State Classification by Fusing Hand-Crafted and Deep Learning EEG Features. IEEE Transactions on Circuits and Systems II(Express Briefs), 2021, 68(4): 1542-1546.
[101] SINGH K, MALHOTRA J.Two-Layer LSTM Network-Based Prediction of Epileptic Seizures Using EEG Spectral Features. Complex and Intelligent Systems, 2022, 8(3): 2405-2418.
[102] WANG Y M, CAO J W, WANG J Z, et al. Epileptic Signal Classification with Deep Transfer Learning Feature on Mean Amplitude Spectrum // Proc of the 41st Annual International Confe-rence of the IEEE Engineering in Medicine and Biology Society. Washington, USA: IEEE, 2019: 2392-2395.
[103] CAO J W, HU D H, WANG Y M, et al. Epileptic Classification with Deep-Transfer-Learning-Based Feature Fusion Algorithm. IEEE Transactions on Cognitive and Developmental Systems, 2022, 14(2): 684-695.
[104] JOSHI V, NANAVATI N.A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders Using Machine Learning. Journal of Biomedical Photonics and Engineering, 2021, 7(4). DOI: 10.18287/JBPE21.07.040201.
[105] SASIDHARAN A, DUTTA K K.Application of Machine-Lear-ning Techniques in Electroencephalography Signals // SAHU M, SINHA G R, eds. Brain and Behavior Computing. Totnes, UK: CRC Press, 2021: 61-84.
[106] SANEI S, CHAMBERS J A. EEG Signal Processing and Machine Learning. New York, USA: John Wiley and Sons, 2021.
[107] SAKKALIS V, CASSAR T, ZERVAKIS M, et al. Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy. Computational Intelligence and Neuroscience, 2008. DOI: 10.1155/2008/462593.
[108] JOSHI C N, PATRICK J.Eyelid Myoclonia with Absences: Routine EEG Is Sufficient to Make a Diagnosis. Seizure, 2007, 16(3): 254-260.
[109] SAKKALIS V, CASSAR T, ZERVAKIS M, et al. A Decision Su-pport Framework for the Discrimination of Children with Controlled Epilepsy Based on EEG Analysis. Journal of Neuroengineering and Rehabilitation, 2010, 7. DOI: 10.1186/1743-0003-7-24.
[110] ADEBIMPE A, AARABI A, BOUREL-PONCHEL E, et al. EEG Resting State Functional Connectivity Analysis in Children with Benign Epilepsy with Centrotemporal Spikes. Frontiers in Neuroscience, 2016, 10. DOI: 10.3389/fnins.2016.00143.
[111] ENGLOT D J, HAN S J, ROLSTON J D, et al. Epilepsy Surgery Failure in Children: A Quantitative and Qualitative Analysis. Journal of Neurosurgery: Pediatrics, 2014, 14(4): 386-395.
[112] CSERPAN D, ROSCH R, LO BIUNDO S P L, et al. Variation of Scalp EEG High Frequency Oscillation Rate with Sleep Stage and Time Spent in Sleep in Patients with Pediatric Epilepsy. Clinical Neurophysiology, 2022, 135: 117-125.
[113] GLABA P, LATKA M, KRAUSE M, et al. Changes in Interictal Pretreatment and Posttreatment EEG in Childhood Absence Epilepsy. Frontiers in Neuroscience, 2020, 14. DOI: 10.3389/fnins.2020.00196.
[114] RIGHI M, BARCARO U, STARITA A, et al. Detection of Signs of Brain Dysfunction in Epileptic Children by Recognition of Transient Changes in the Correlation of Seizure-Free EEG. Brain Topography, 2008, 21(1): 43-51.
[115] THATCHER R W, WALKER R A, BIVER C J, et al. Quantitative EEG Normative Databases: Validation and Clinical Correlation. Journal of Neurotherapy, 2003, 7(3/4): 87-121.
[116] KEVRIC J, SUBASI A.The Effect of Multiscale PCA De-Noising in Epileptic Seizure Detection. Journal of Medical Systems, 2014, 38(10). DOI: 10.1007/s10916-014-0131-0.
[117] SARDOUIE S H, ALBERA L, SHAMSOLLAHI M B, et al. An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising. IEEE Signal Processing Letters, 2015, 22(8): 1198-1202.
[118] KINNEY-LANG E, SPYROU L, EBIED A, et al. Tensor-Driven Extraction of Developmental Features from Varying Paediatric EEG Datasets. Journal of Neural Engineering, 2018, 15(4). DOI: 10.1088/1741-2552/aac664.
[119] WEN T X, ZHANG Z N.Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals. IEEE Access, 2018, 6: 25399-25410.
[120] RAJAGURU H, PRABHAKAR S K. KNN Classifier and K-means Clustering for Robust Classification of Epilepsy from EEG Signals. Hamburg, Germany: Anchor Academic Publishing, 2017.
[121] LI G, JUNG J J.Seizure Detection from Multi-channel EEG Using Entropy-Based Dynamic Graph Embedding. Artificial Intelligence in Medicine, 2021, 122. DOI: 10.1016/j.artmed.2021.102201.
[122] THOMAS E M, TEMKO A, LIGHTBODY G, et al. Gaussian Mix-ture Models for Classification of Neonatal Seizures Using EEG. Physiological Measurement, 2010, 31(7): 1047-1064.
[123] RIJO R, SILVA C, PEREIRA L, et al. Decision Support System to Diagnosis and Classification of Epilepsy in Children. Journal of Universal Computer Science, 2014, 20(6): 907-923.
[124] QARAQE M, ISMAIL M, SERPEDIN E, et al. Epileptic Seizure Onset Detection Based on EEG and ECG Data Fusion. Epilepsy and Behavior, 2016, 58: 48-60.
[125] LIU Y X, ZHOU W D, YUAN Q, et al. Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 20(6): 749-755.
[126] WANG Y X, CAO J W, LAI X P, et al. Epileptic State Classification for Seizure Prediction with Wavelet Packet Features and Random Forest // Proc of the Chinese Control And Decision Conference. Washington USA: IEEE, 2019: 3983-3987.
[127] YUAN S S, LIU J X, SHANG J L, et al. The Earth Mover's Distance and Bayesian Linear Discriminant Analysis for Epileptic Seizure Detection in Scalp EEG. Biomedical Engineering Letters, 2018, 8: 373-382.
[128] IBRAHIM S W, DJEMAL R, ALSUWAILEM A, et al. Electroencephalography(EEG)-Based Epileptic Seizure Prediction Using Entropy and K-nearest Neighbor(KNN). Communications in Science and Technology, 2017, 2(1): 6-10.
[129] JANA G C, SHARMA R, AGRAWAL A.A 1D-CNN-Spectrogram Based Approach for Seizure Detection from EEG Signal. Procedia Computer Science, 2020, 167: 403-412.
[130] AVCU M T, ZHANG Z, CHAN D W S. Seizure Detection Using Least EEG Channels by Deep Convolutional Neural Network // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington, USA: IEEE, 2019: 1120-1124.
[131] FENG Y M, ZHENG R Z, CUI X N, et al. 3D Residual-Attention-Deep-Network-Based Childhood Epilepsy Syndrome Classification. Knowledge-Based Systems, 2022, 248. DOI: 10.1016/j.knosys.2022.108856V.
[132] XU Z D, WANG T L, CAO J W, et al. BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1734-1743.
[133] TSIOURIS Κ Μ, PEZOULAS V C, ZERVAKIS M, et al. A Long Short-Term Memory Deep Learning Network for the Prediction of Epileptic Seizures Using EEG Signals. Computers in Biology and Medicine, 2018, 99: 24-37.
[134] RAGHU S, SRIRAAM N, TEMEL Y, et al. EEG Based Multi-class Seizure Type Classification Using Convolutional Neural Network and Transfer Learning. Neural Networks, 2020, 124: 202-212.
[135] TIAN X B, DENG Z H, YING W N, et al. Deep Multi-view Feature Learning for EEG-Based Epileptic Seizure Detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(10): 1962-1972.
[136] JIANG T J, XU Z D, CAO J W, et al. BECT Spike Detection Based on Novel Multichannel Data Weighted Fusion Algorithm. IEEE Transactions on Circuits and Systems II(Express Briefs), 2022, 69(11): 4613-4617.
[137] MALDONADO R, HARABAGIU S M.Active Deep Learning for the Identification of Concepts and Relations in Electroencephalography Reports. Journal of Biomedical Informatics, 2019, 98. DOI: 10.1016/j.jbi.2019.103265.
[138] CAO J W, CHEN L, HU D H, et al. Unsupervised Eye Blink Arti-fact Detection from EEG with Gaussian Mixture Model. IEEE Journal of Biomedical and Health Informatics, 2021, 25(8): 2895-2905.
[139] WANG M, WANG J H, CUI X N, et al. Multidimensional Feature Optimization Based Eye Blink Detection under Epileptiform Discharges. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 905-914.
[140] WANG M, CUI X N, WANG T L, et al. Eye Blink Artifact Detection Based on Multi-dimensional EEG Feature Fusion and Optimization. Biomedical Signal Processing and Control, 2023, 83. DOI: 10.1016/j.bspc.2023.104657.
[141] WESIERSKI D, RUFUIE M R, MILCZAREK O, et al. Rating by Detection: An Artifact Detection Protocol for Rating EEG Quality with Average Event Duration. Journal of Neural Engineering, 2023, 20(2). DOI: 10.1088/1741-2552/acbabe.
[142] WANG Z M, WU D P, DONG F, et al. A Novel Spike Detection Algorithm Based on Multi-channel of BECT EEG Signals. IEEE Transactions on Circuits and Systems II(Express Briefs), 2020, 67(12): 3592-3596.
[143] MONSOOR T, ZHANG Y P, DAIDA A, et al. Optimizing Detection and Deep Learning-Based Classification of Pathological High-Frequency Oscillations in Epilepsy. Clinical Neurophysiology, 2023, 154: 129-140.
[144] CAO J W, FENG Y M, ZHENG R Z, et al. Two-Stream Attention 3-D Deep Network-Based Childhood Epilepsy Syndrome Cla-ssification. IEEE Transactions on Instrumentation and Measurement, 2022, 72. DOI: 10.1109/TIM.2022.3220287.
[145] MISIUNAS A V M, MEŠKAUSKAS T, SAMAITIENE R.Algorithm for Automatic EEG Classification According to the Epilepsy Type: Benign Focal Childhood Epilepsy and Structural Focal Epilepsy. Biomedical Signal Processing and Control, 2019, 48: 118-127.
[146] JIANG T J, ZHU J H, HU D H, et al. Early Seizure Detection in Childhood Focal Epilepsy with Electroencephalogram Feature Fusion on Deep Autoencoder Learning and Channel Correlations. Multidimensional Systems and Signal Processing, 2022, 33(4): 1273-1293.
[147] SHOJI T, YOSHIDA N, TANAKA T.Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients with a Compact Convolutional Neural Network. Biomedical Signal Processing and Control, 2021, 70. DOI: 10.1016/j.bspc.2021.103013.
[148] CAO J W, CHEN Y H, ZHENG R Z, et al. DSMN-ESS: Dual-Stream Multi-task Network for Epilepsy Syndrome Classification and Seizure Detection. IEEE Transactions on Instrumentation and Measurement, 2023. DOI: 10.1109/TIM.2023.3307724.
[149] CUI X N, WANG T L, LAI X P, et al. Cross-Subject Seizure Detection by Joint-Probability-Discrepancy-Based Domain Adaptation. IEEE Transactions on Instrumentation and Measurement, 2023, 72. DOI: 10.1109/TIM.2023.3248101.
[150] CUI X N, CAO J W, LAI X P, et al. Cluster Embedding Joint-Probability-Discrepancy Transfer for Cross-Subject Seizure Detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 31: 593-605.
[151] CHINAPPEN D M, XIAO G, JING J, et al. Spike Height Improves Prediction of Future Seizure Risk. Clinical Neurophysiology, 2023, 150: 49-55.
[152] KIRAL-KORNEK I, ROY S, NURSE E, et al. Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System. EBioMedicine, 2018, 27: 103-111.
[153] 韩长明,彭福来,陈财,等.基于脑电信号的癫痫发作预测研究进展.生物医学工程学杂志, 2021, 38(6): 1193-1202.
(HAN C M, PENG F L, CHEN C, et al. Research Progress of Epileptic Seizure Predictions Based on Electroencephalogram Signals. Journal of Biomedical Engineering, 2021, 38(6): 1193-1202.)
[154] ZHAO X, WANG X H, CHEN C, et al. A Knowledge-Based Approach for Automatic Quantification of Epileptiform Activity in Children with Electrical Status Epilepticus During Sleep. Journal of Neural Engineering, 2020, 17(4). DOI: 10.1088/1741-2552/aba6dd.
[155] ZHOU W, ZHAO X, WANG X H, et al. A Hybrid Expert System for Individualized Quantification of Electrical Status Epilepti-cus During Sleep Using Biogeography-Based Optimization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1920-1930.
[156] ABD EL-SAMIE F E, ALOTAIBY T N, KHALID M I, et al. A Review of EEG and MEG Epileptic Spike Detection Algorithms. IEEE Access, 2018, 6: 60673-60688.
[157] WANG J H, CAO J W, HU D H, et al. Eye Blink Artifact Detection with Novel Optimized Multi-dimensional Electroencephalogram Features. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1494-1503.
[158] JIANG T J, WU D P, GAO F, et al. Improved Spike Detection Algorithm Based on Multi-template Matching and Feature Extraction. IEEE Transactions on Circuits and Systems II(Express Briefs), 2022, 69(1): 249-253.
[159] SHLOBIN N A, SINGH G, NEWTON C R, et al. Classifying Epilepsy Pragmatically: Past, Present, and Future. Journal of the Neurological Sciences, 2021, 427. DOI: 10.1016/j.jns.2021.117515.
[160] ARIAS C R, DURóN R M, DELGADO-ESCUETA A V. Identification of New Epilepsy Syndromes Using Machine Learning // Proc of the IEEE 39th Central America and Panama Convention. Washington, USA: IEEE, 2019. DOI: 10.1109/CONCAPANXXXIX47272.2019.8977043.
[161] HANDA P, MATHUR M, GOEL N. EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection. SN Computer Science, 2023, 4(5). DOI: 10.1007/s42979-023-01958-z.
[162] MCCALLAN N, DAVIDSON S, NG K Y, et al. Epileptic Multi-seizure Type Classification Using Electroencephalogram Signals from the Temple University Hospital Seizure Corpus: A Review. Expert Systems with Applications, 2023, 234. DOI: 10.1016/j.eswa.2023.121040.
[163] LARSEN P M, WüSTENHAGEN S, TERNEY D, et al. Duration of Epileptic Seizure Types: A Data-Driven Approach. Epilepsia, 2023, 64(2): 469-478.
[164] IASEMIDIS L D. Epileptic Seizure Prediction and Control. IEEE Transactions on Biomedical Engineering, 2003, 50(5): 549-558.
[165] COOK M J, O'BRIEN T J, BERKOVIC S F, et al. Prediction of Seizure Likelihood with a Long-Term, Implanted Seizure Advisory System in Patients with Drug-Resistant Epilepsy: A First-in-Man Study. The Lancet Neurology, 2013, 12(6): 563-571.
[166] SCHLEGEL K, KLEYKO D, BRINKMANN B H, et al. Lessons from a Challenge on Forecasting Epileptic Seizures from Non-cerebral Signals. Nature Machine Intelligence, 2024, 6: 243-244.
[167] ZHENG R Z, CAO J W, FENG Y M, et al. Seizure Prediction Analysis of Infantile Spasms. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 31: 366-376.
[168] NATU M, BACHUTE M, GITE S, et al. Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches. Computational and Mathematical Methods in Medicine, 2022. DOI: 10.1155/2022/7751263.
[169] CANTALUPO G, PAVLIDIS E, BENICZKY S, et al. Quantitative EEG Analysis in Encephalopathy Related to Status Epilepti-cus during Slow Sleep. Epileptic Disorders, 2019, 21(S1): S31-S40.
[170] BALARAM N, JOSE J, GAFOOR A V, et al. Classification of Electrical Status Epilepticus in Sleep Based on EEG Patterns and Spatiotemporal Mapping of Spikes. Epileptic Disorders, 2022, 24(6): 1060-1072.
[171] KRAMER U, SAGI L, GOLDBERG-STERN H, et al. Clinical Spectrum and Medical Treatment of Children with Electrical Status Epilepticus in Sleep(ESES). Epilepsia, 2009, 50(6): 1517-1524.
[172] CHAVAKULA V, FERNáNDEZ I S, PETERS J M, et al. Automated Quantification of Spikes. Epilepsy and Behavior, 2013, 26(2): 143-152.
[173] JOSHI C N, CHAPMAN K E, BEAR J J, et al. Semiautomated Spike Detection Software Persyst 13 Is Noninferior to Human Readers When Calculating the Spike-Wave Index in Electrical Status Epilepticus in Sleep. Journal of Clinical Neurophysiology, 2018, 35(5): 370-374.
[174] NONCLERCQ A, FOULON M, VERHEULPEN D, et al. Spike Detection Algorithm Automatically Adapted to Individual Patients Applied to Spike and Wave Percentage Quantification. Neurophysiologie Clinique/Clinical Neurophysiology, 2009, 39(2): 123-131.
[175] WIWATTANADITTAKUL N, DEPOSITARIO-CABACAR D, ZE-LLEKE T G. Electrical Status Epilepticus in Sleep(ESES)-Treatment Pattern and EEG Outcome in Children with Very High Spike-Wave Index. Epilepsy and Behavior, 2020, 105. DOI: 10.1016/j.yebeh.2020.106965. |
2024, Vol. 37 
