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Brief Report
October 21, 2019

Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation

Author Affiliations
  • 1Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
  • 2School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore
  • 3Department of Neurology, Yale University School of Medicine, New Haven, Connecticut
  • 4Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
  • 5Department of Neurology, University of Manitoba, Winnipeg, Manitoba, Canada
  • 6Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
  • 7Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy
  • 8Department of Neurology, University of Pennsylvania General Hospital, Boston, Massachusetts
JAMA Neurol. 2020;77(1):103-108. doi:10.1001/jamaneurol.2019.3485
Key Points

Question  Can a computer program be trained to identify interictal epileptiform discharges and classify an electroencephalogram as containing interictal epileptiform discharges with accuracy equivalent or superior to that of physicians with subspecialty training in clinical neurophysiology?

Findings  In this diagnostic study of interictal epileptiform discharges, a deep neural network was trained using 9571 scalp electroencephalogram recordings. The algorithm appeared to perform at or above the accuracy, sensitivity, and specificity of fellowship-trained clinical experts.

Meaning  This computer program appeared to be able to classify electroencephalograms and detect individual interictal epileptiform discharges more accurately than human experts and may help with diagnostic testing for epilepsy and warn of clinical decline in critically ill patients, particularly in settings without available electroencephalogram expertise.


Importance  Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability.

Objective  To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs.

Design, Setting, and Participants  A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built.

Main Outcomes and Measures  SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation.

Results  SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865).

Conclusions and Relevance  In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

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