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Abstract Details

Accuracy of Automated Machine Learning Software in Identifying EEGs with Prolonged Seizures
Epilepsy/Clinical Neurophysiology (EEG)
P6 - Poster Session 6 (12:00 PM-1:00 PM)

Prolonged seizures are markers of seizure severity, risk of transformation into status epilepticus, and medical morbidity. Early recognition of prolonged seizures permits intervention and reduces morbidity.

To demonstrate that combining automatic processing of EEG data using high performance machine learning algorithms with manual review by expert annotators can quickly identify subjects with prolonged seizures.

We triaged the TUH EEG Corpus, an open source database of EEGs, by running a state-of-the-art hybrid LSTM-based deep learning system. Then, we postprocessed the output to identify high confidence hypotheses for seizures that were greater than three minutes in duration.

The triaging method selected 25 subjects for further review. 17 subjects had seizures; only 5 met criteria for seizures greater than 3 minutes. 11 subjects did not have a prior diagnosis of epilepsy. Among these, 63% had acute respiratory failure and 36% had cardiac arrest leading to seizures secondary to anoxic brain injury. 18 (72%) EEGs were obtained in long-term monitoring (LTM), 1 (4%) in the epilepsy monitoring unit (EMU), and 6 (24%) as a routine EEG (rEEG). 72.2% of seizures in LTM were identified correctly versus 66.7% in rEEGs. Of the 9 subjects who were deceased, 7 (78%) had been on LTM. The seizure detection algorithm misidentified seizures in 7 subjects (28%). A total of 22 (88%) subjects had some ictal pattern. Patterns mistaken for seizure activity included muscle artifact, generalized periodic discharges, generalized spike-and-wave, triphasic waves, and interestingly, an EEG recording captured during CPR.

This hybrid approach, which combines state-of-the-art machine learning seizure detection software with human annotation, successfully identified prolonged seizures in 72% of subjects; 88% had ictal patterns. Prolonged seizures were more common in LTM subjects than the EMU and were associated with acute cardiac or pulmonary insult.

Rebecca T. Hsu, MD (Thomas Jefferson University, Department of Neurology)
Dr. Hsu has nothing to disclose.
Destiny Lee Marquez, MD (mount sinai ) Dr. Marquez has nothing to disclose.
Mercedes P. Jacobson, MD (Temple University) The institution of Dr. Jacobson has received research support from Engage. The institution of Dr. Jacobson has received research support from XENON. The institution of Dr. Jacobson has received research support from SK Life Sciences.
Hannah Castaldi No disclosure on file
Samuel Buckland No disclosure on file
Vinit Shah No disclosure on file
Joseph Picone No disclosure on file