Seizure Detection Assessments Using a Large Dataset.

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Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset.

Abstract
PURPOSE: To compare the seizure detection performance of three expert humans and two computer algorithms in a large set of epilepsy monitoring unit EEG recordings.
METHODS: One hundred twenty prolonged EEGs, 100 containing clinically reported EEG-evident seizures, were evaluated. Seizures were marked by the experts and algorithms. Pairwise sensitivity and false-positive rates were calculated for each human-human and algorithm-human pair. Differences in human pairwise performance were calculated and compared with the range of algorithm versus human performance differences as a type of statistical modified Turing test.
RESULTS: A total of 411 individual seizure events were marked by the experts in 2,805 hours of EEG. Mean, pairwise human sensitivities and false-positive rates were 84.9%, 73.7%, and 72.5%, and 1.0, 0.4, and 1.0/day, respectively. Only the Persyst 14 algorithm was comparable with humans-78.2% and 1.0/day. Evaluation of pairwise differences in sensitivity and false-positive rate demonstrated that Persyst 14 met statistical noninferiority criteria compared with the expert humans.
CONCLUSIONS: Evaluating typical prolonged EEG recordings, human experts had a modest level of agreement in seizure marking and low false-positive rates. The Persyst 14 algorithm was statistically noninferior to the humans. For the first time, a seizure detection algorithm and human experts performed similarly.

PMID: 32472781 [PubMed – as supplied by publisher]

J Clin Neurophysiol. 2020 May 27;:

Authors: Scheuer ML, Wilson SB, Antony A, Ghearing G, Urban A, Bagić AI

 

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