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Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation
Jin Jing1, Wendong Ge1, Shenda Hong1
1From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA.
A new AI algorithm, SPaRCNet, matches expert performance in detecting seizures (SZs) and similar brain activity patterns on EEGs. This tool could expedite EEG reviews for critical care patients.
Area of Science:
- Neurology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Seizures (SZs) and related brain activity patterns pose significant risks, including brain damage and mortality, especially when prolonged.
- A scarcity of qualified EEG interpreters limits timely diagnosis and intervention.
- Previous automated methods for classifying these events have been hindered by small datasets and lack of generalizable performance.
Purpose of the Study:
- To develop and validate a computer algorithm capable of classifying SZs and other SZ-like events with expert-level reliability.
- To create a tool that accurately identifies "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, periodic, and rhythmic delta activity.
- To differentiate IIIC patterns from non-IIIC patterns in EEG data.
Main Methods:
- A deep neural network, SPaRCNet, was trained using 6,095 scalp EEGs from 2,711 patients.
- Independent datasets of 50,697 EEG segments were annotated by 20 fellowship-trained neurophysiologists.
- SPaRCNet's performance was evaluated against expert neurophysiologists using sensitivity, specificity, precision, calibration, ROC curves, and PRC curves.
Main Results:
- SPaRCNet demonstrated performance matching or exceeding most experts in classifying IIIC events.
- The algorithm showed high accuracy across various classes, including seizures, lateralized periodic discharges (LPD), generalized periodic discharges (GPD), lateralized rhythmic delta activity (LRDA), and generalized rhythmic delta activity (GRDA).
- SPaRCNet exceeded the performance of a significant percentage of experts in ROC, PRC, and calibration metrics for all tested pattern classes.
Conclusions:
- SPaRCNet is the first algorithm to achieve expert-level performance in detecting SZs and similar events in a representative EEG sample.
- This AI tool shows potential for expediting EEG interpretation in clinical settings.
- Further development of SPaRCNet could significantly aid in the timely management of patients with critical neurological conditions.
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