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Seizure Detection Algorithms in Critically Ill Children: A Comparative Evaluation
Farah Din1, Saptharishi Lalgudi Ganesan2,3, Tomoyuki Akiyama4
1Division of Neurology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
Insights
Commercial seizure detection algorithms show performance comparable to experts in critically ill children. These tools may serve as early warning systems for timely seizure identification.
Area of Science:
- Pediatric Neurology
- Medical Technology
- Critical Care Medicine
Background:
- Seizure detection in critically ill children is challenging.
- Electroencephalography (EEG) monitoring is crucial for diagnosis.
- Quantitative EEG (qEEG) trends offer novel visualization methods.
Purpose of the Study:
- To evaluate the diagnostic accuracy of commercial seizure detection algorithms.
- To compare algorithm performance against expert EEG interpretation.
- To assess the utility of these algorithms in critically ill pediatric patients.
Main Methods:
- Continuous EEG recordings from critically ill children were analyzed.
- Seizures were identified by neurophysiologists and EEG experts.
- Four commercial seizure detection algorithms (ICTA-S, NB, Persyst 11, Persyst 13) were evaluated.
- Algorithm sensitivity and specificity were compared to expert interpretations of raw EEG and qEEG displays (amplitude-integrated EEG and color density spectral array).
Main Results:
- Persyst 11 (75.9%) and Persyst 13 (74.4%) showed sensitivity comparable to experts (76.5% CDS, 73.7% aEEG).
- NB algorithm had high sensitivity (92.3%) but a high false-positive rate (126.3/day).
- Persyst 11 demonstrated the best balance between sensitivity and false-positive rate (5.1/day).
Conclusions:
- Certain commercial seizure detection algorithms perform comparably to expert EEG analysis.
- These algorithms can function as valuable early warning systems.
- Timely seizure identification in critically ill children may be improved through the use of these technologies.
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 amplitude-integrated 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.
