% 0期刊文章%金晶%一个Wendong Ge %申香港% Marta盒饭费尔南德斯%一个林甄% Chaoqi杨% Sungtae一个%达成的亚伦f %排列Herlopian % Ioannis卡拉奇%乔纳森·j·哈尔福德%马库斯·c·Ng %艾米丽·l·约翰逊%布莱恩·l·Appavu %一个王妃A . Sarkis % Gamaleldin奥斯曼%一个彼得·w·卡普兰%莫妮卡b . Dhakar % Lakshman Arcot Jayagopal %一个奥尔加Taraschenko Zubeda酋长% %一个莎拉·施密特% Hiba A海德尔%珍妮弗·A·金% b Christa。斯威舍%尼古拉斯%加斯帕德Mackenzie C. Cervenka %A Andres A. Rodriguez Ruiz %A Jong Woo Lee %A Mohammad Tabaeizadeh %A Emily J. Gilmore %A Kristy Nordstrom %A Ji Yeoun Yoo %A Manisha G. Holmes %A Susan T. Herman %A Jennifer A. Williams %A Jay Pathmanathan %A Fábio A. Nascimento %A Ziwei Fan %A Samaneh Nasiri %A Mouhsin M. Shafi %A Sydney S. Cash %A Daniel B. Hoch %A Andrew J. Cole %A Eric S. Rosenthal %A Sahar F. Zafar %A Jimeng Sun %A M. Brandon Westover %T Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation %D 2023 %R 10.1212/WNL.0000000000207127 %J Neurology %P 10.1212/WNL.0000000000207127 %X Background and Objectives: Seizures and other seizure-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret electroencephalography (EEG) data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify seizures and other seizure-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying seizures and seizure-like events, known as “ictal-interictal-injury-continuum” (IIIC) patterns on EEG, including seizures (SZ), lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns.Methods: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test datasets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed via the calibration index, and by the percentage of experts whose operating points were below the model’s receiver operating characteristic curves (ROC) and precision recall curves (PRC) for the 6 pattern classes.Results: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and “Other” classes, SPaRCNet exceeds the following percentages of 20 experts – ROC: 45%, 20%, 50%, 75%, 55%, 40%; PRC: 50%, 35%, 50%, 90%, 70%, 45%; and calibration: 95%, 100%, 95%, 100%, 100%, 80%, respectively.Discussion: SPaRCNet is the first algorithm to match expert performance in detecting seizures and other seizure-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for expedited review of EEGs. %U //www.ez-admanager.com/content/neurology/early/2023/03/06/WNL.0000000000207127.full.pdf