Deep active learning for Interictal Ictal Injury Continuum EEG patterns
Ge W, Jing J, An S, Herlopian A, Ng M, Struck AF, Appavu B, Johnson EL, Osman G, Haider HA, Karakis I, Kim JA, Halford JJ, Dhakar MB, Sarkis RA, Swisher CB, Schmitt S, Lee JW, Tabaeizadeh M, Rodriguez A, Gaspard N, Gilmore E, Herman ST, Kaplan PW, Pathmanathan J, Hong S, Rosenthal ES, Zafar S, Sun J, Westover M. Deep active learning for Interictal Ictal Injury Continuum EEG patterns. Journal Of Neuroscience Methods 2020, 351: 108966. PMID: 33131680, PMCID: PMC8135050, DOI: 10.1016/j.jneumeth.2020.108966.Peer-Reviewed Original ResearchConceptsConvolutional neural networkIll patientsActive learningLarge labeled datasetExpert-level performanceDeep active learningLarge EEG datasetsPseudo-labeled dataUse of ALElectroencephalography patternsPatient careQuery criteriaLabeled datasetLabel spreadingEEG patternsPatientsExpert labelsClass balancingNeural networkAvailable labelsVector representationQueriesInformative examplesAL approachEEG datasetDevelopment of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation
Jing J, Sun H, Kim JA, Herlopian A, Karakis I, Ng M, Halford JJ, Maus D, Chan F, Dolatshahi M, Muniz C, Chu C, Sacca V, Pathmanathan J, Ge W, Dauwels J, Lam A, Cole AJ, Cash SS, Westover MB. Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation. JAMA Neurology 2020, 77: 103-108. PMID: 31633740, PMCID: PMC6806668, DOI: 10.1001/jamaneurol.2019.3485.Peer-Reviewed Original ResearchMeSH KeywordsElectroencephalographyEpilepsyHumansNeural Networks, ComputerSensitivity and SpecificitySignal Processing, Computer-AssistedSoftware