2024
Improving Clinician Performance in Classifying EEG Patterns on the Ictal-Interictal Injury Continuum Using Interpretable Machine Learning.
Barnett A, Guo Z, Jing J, Ge W, Kaplan P, Kong W, Karakis I, Herlopian A, Jayagopal L, Taraschenko O, Selioutski O, Osman G, Goldenholz D, Rudin C, Westover M. Improving Clinician Performance in Classifying EEG Patterns on the Ictal-Interictal Injury Continuum Using Interpretable Machine Learning. NEJM AI 2024, 1 PMID: 38872809, PMCID: PMC11175595, DOI: 10.1056/aioa2300331.Peer-Reviewed Original Research
2020
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 dataset
2016
Extreme delta brush evolving into status epilepticus in a patient with anti-NMDA encephalitis
Herlopian A, Rosenthal ES, Chu CJ, Cole AJ, Struck AF. Extreme delta brush evolving into status epilepticus in a patient with anti-NMDA encephalitis. Epilepsy & Behavior Reports 2016, 7: 69-71. PMID: 28616386, PMCID: PMC5459970, DOI: 10.1016/j.ebcr.2016.09.002.Peer-Reviewed Original Research