In-depth performance analysis of an EEG based neonatal seizure detection algorithm

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Mathieson, Sean
Rennie, Janet
Livingstone, Vicki
Temko, Andriy
Low, Evonne
Pressler, R. M.
Boylan, Geraldine B.
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Objective: To describe a novel neurophysiology based performance analysis of automated seizure detection algorithms for neonatal EEG to characterize features of detected and non-detected seizures and causes of false detections to identify areas for algorithmic improvement. Methods: EEGs of 20 term neonates were recorded (10 seizure, 10 non-seizure). Seizures were annotated by an expert and characterized using a novel set of 10 criteria. ANSeR seizure detection algorithm (SDA) seizure annotations were compared to the expert to derive detected and non-detected seizures at three SDA sensitivity thresholds. Differences in seizure characteristics between groups were compared using univariate and multivariate analysis. False detections were characterized. Results: The expert detected 421 seizures. The SDA at thresholds 0.4, 0.5, 0.6 detected 60%, 54% and 45% of seizures. At all thresholds, multivariate analyses demonstrated that the odds of detecting seizure increased with 4 criteria: seizure amplitude, duration, rhythmicity and number of EEG channels involved at seizure peak. Major causes of false detections included respiration and sweat artefacts or a highly rhythmic background, often during intermediate sleep. Conclusion: This rigorous analysis allows estimation of how key seizure features are exploited by SDAs. Significance: This study resulted in a beta version of ANSeR with significantly improved performance.
Automated seizure detection , Neonatal seizures , Detection algorithm
Mathieson, S., Rennie, J., Livingstone, V., Temko, A., Low, E., Pressler, R. M. and Boylan, G. B. (2016), 'In-depth performance analysis of an EEG based neonatal seizure detection algorithm', Clinical Neurophysiology, 127(5), pp. 2246-2256. DOI: 10.1016/j.clinph.2016.01.026