Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation

Jin Jing, Wendong Ge, Shenda Hong, Marta Bento Fernandes, Zhen Lin, Chaoqi Yang, Sungtae An, Aaron F. Struck, Aline Herlopian, Ioannis Karakis, Jonathan J. Halford, Marcus C. Ng, Emily L. Johnson, Brian L. Appavu, Rani A. Sarkis, Gamaleldin Osman, Peter W. Kaplan, Monica B. Dhakar, Lakshman Arcot Jayagopal, Zubeda SheikhOlga Taraschenko, Sarah Schmitt, Hiba A. Haider, Jennifer A. Kim, Christa B. Swisher, Nicolas Gaspard, Mackenzie C. Cervenka, Andres A. Rodriguez Ruiz, Jong Woo Lee, Mohammad Tabaeizadeh, Emily J. Gilmore, Kristy Nordstrom, Ji Yeoun Yoo, Manisha G. Holmes, Susan T. Herman, Jennifer A. Williams, Jay Pathmanathan, Fábio A. Nascimento, Ziwei Fan, Samaneh Nasiri, Mouhsin M. Shafi, Sydney S. Cash, Daniel B. Hoch, Andrew J. Cole, Eric S. Rosenthal, Sahar F. Zafar, Jimeng Sun, M. Brandon Westover

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background and Objectives Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret 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 SZs and other SZ-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 SZs and SZ-like events, known as “ictal-interictal-injury continuum” (IIIC) patterns on EEG, including SZs, 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 data sets 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 by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) 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%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. Discussion SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. Classification of Evidence This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Original languageEnglish (US)
Pages (from-to)E1750-E1762
JournalNeurology
Volume100
Issue number17
DOIs
StatePublished - Apr 25 2023

ASJC Scopus subject areas

  • Clinical Neurology

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