Seizure detection algorithms in critically ill children: A comparative evaluation

Farah Din, Saptharishi Lalgudi Ganesan, Tomoyuki Akiyama, Craig P. Stewart, Ayako Ochi, Hiroshi Otsubo, Cristina Go, Cecil D. Hahn

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Objectives: To evaluate the performance of commercially available seizure detection algorithms in critically ill children. Design: Diagnostic accuracy comparison between commercially available seizure detection algorithms referenced to electroencephalography experts using quantitative electroencephalography trends. Setting: Multispecialty quaternary children's hospital in Canada. Subjects: Critically ill children undergoing electroencephalography monitoring. Interventions: Continuous raw electroencephalography recordings (n = 19) were analyzed by a neurophysiologist to identify seizures. Those recordings were then converted to quantitative electroencephalography displays (amplitude-integrated electroencephalography and color density spectral array) and evaluated by six independent electroencephalography experts to determine the sensitivity and specificity of the amplitude-integrated electroencephalography and color density spectral array displays for seizure identification in comparison to expert interpretation of raw electroencephalography data. Those evaluations were then compared with four commercial seizure detection algorithms: ICTA-S (Stellate Harmonie Version 7; Natus Medical, San Carlos, CA), NB (Stellate Harmonie Version 7; Natus Medical), Persyst 11 (Persyst Development, Prescott, AZ), and Persyst 13 (Persyst Development) to determine sensitivity and specificity in comparison to amplitude-integrated electroencephalography and color density spectral array. Measurements and Main Results: Of the 379 seizures identified on raw electroencephalography, ICTA-S detected 36.9%, NB detected 92.3%, Persyst 11 detected 75.9%, and Persyst 13 detected 74.4%, whereas electroencephalography experts identified 76.5% of seizures using color density spectral array and 73.7% using amplitude-integrated electroencephalography. Daily false-positive rates averaged across all recordings were 4.7 with ICTA-S, 126.3 with NB, 5.1 with Persyst 11, 15.5 with Persyst 13, 1.7 with color density spectral array, and 1.5 with amplitudeintegrated electroencephalography. Both Persyst 11 and Persyst 13 had sensitivity comparable to that of electroencephalography experts using amplitude-integrated electroencephalography and color density spectral array. Although Persyst 13 displayed the highest sensitivity for seizure count and seizure burden detected, Persyst 11 exhibited the best trade-off between sensitivity and false-positive rate among all seizure detection algorithms. Conclusions: Some commercially available seizure detection algorithms demonstrate performance for seizure detection that is comparable to that of electroencephalography experts using quantitative electroencephalography displays. These algorithms may have utility as early warning systems that prompt review of quantitative electroencephalography or raw electroencephalography tracings, potentially leading to more timely seizure identification in critically ill patients.

Original languageEnglish
Pages (from-to)545-552
Number of pages8
JournalCritical care medicine
DOIs
Publication statusPublished - Apr 1 2020

Keywords

  • Automated seizure detection
  • Diagnostic accuracy
  • Electrographic seizures
  • Pediatric critical care
  • Quantitative electroencephalography

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine

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