Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset. Issue 5 (September 2021)

• Record Type: Journal Article
• Authors: Scheuer, Mark L.; Wilson, Scott B.; Antony, Arun; Ghearing, Gena; Urban, Alexandra; Bagić, Anto I.
• Is Part Of: Journal of clinical neurophysiology. Volume 38:Issue 5(2021)
• Publication Date: 2021-09
• Subjects: EEG -- Automated seizure detection -- Artificial neural network -- Deep learning -- Noninferiority -- Interreader agreement; Electroencephalography -- Periodicals; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electroencephalography; Periodicals; 612.805
• DOI: 10.1097/WNP.0000000000000709
• Languages: English
• ISSNs: 0736-0258

Abstract

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.