OBJECTIVE: To validate an improved automated electroencephalography (EEG)-based neonatal seizure detection algorithm (NeoGuard) in an independent data set. METHODS: EEG background was classified into eight grades based on the evolution of discontinuity and presence of sleep-wake cycles. Patients were further sub-classified into two groups; gpI: mild to moderate (grades 1-5) and gpII: severe (grades 6-8) EEG background abnormalities. Seizures were categorised as definite and dubious. Seizure characteristics were compared between gpI and gpII. The algorithm was tested on 756 h of EEG data from 24 consecutive neonates (median 25 h per patient) with encephalopathy and recorded seizures during continuous monitoring (cEEG). No selection was made regarding the quality of EEG or presence of artefacts. RESULTS: Seizure amplitudes significantly decreased with worsening EEG background. Seizures were detected with a total sensitivity of 61.9% (1285/2077). The detected seizure burden was 66,244/97,574 s (67.9%). Sensitivity per patient was 65.9%, with a mean positive predictive value (PPV) of 73.7%. After excluding four patients with severely abnormal EEG background, and predominantly having dubious seizures, the algorithm showed a median sensitivity per patient of 86.9%, PPV of 89.5% and false positive rate of 0.28 h(-1). Sensitivity tended to be better for patients in gpI. CONCLUSIONS: The algorithm detects neonatal seizures well, has a good PPV and is suited for cEEG monitoring. Changes in electrographic characteristics such as amplitude, duration and rhythmicity in relation to deteriorating EEG background tend to worsen the performance of automated seizure detection. SIGNIFICANCE: cEEG monitoring is important for detecting seizures in the neonatal intensive care unit (NICU). Our automated algorithm reliably detects neonatal seizures that are likely to be clinically most relevant, as reflected by the associated EEG background abnormality.
OBJECTIVE: To validate an improved automated electroencephalography (EEG)-based neonatal seizure detection algorithm (NeoGuard) in an independent data set. METHODS: EEG background was classified into eight grades based on the evolution of discontinuity and presence of sleep-wake cycles. Patients were further sub-classified into two groups; gpI: mild to moderate (grades 1-5) and gpII: severe (grades 6-8) EEG background abnormalities. Seizures were categorised as definite and dubious. Seizure characteristics were compared between gpI and gpII. The algorithm was tested on 756 h of EEG data from 24 consecutive neonates (median 25 h per patient) with encephalopathy and recorded seizures during continuous monitoring (cEEG). No selection was made regarding the quality of EEG or presence of artefacts. RESULTS:Seizure amplitudes significantly decreased with worsening EEG background. Seizures were detected with a total sensitivity of 61.9% (1285/2077). The detected seizure burden was 66,244/97,574 s (67.9%). Sensitivity per patient was 65.9%, with a mean positive predictive value (PPV) of 73.7%. After excluding four patients with severely abnormal EEG background, and predominantly having dubious seizures, the algorithm showed a median sensitivity per patient of 86.9%, PPV of 89.5% and false positive rate of 0.28 h(-1). Sensitivity tended to be better for patients in gpI. CONCLUSIONS: The algorithm detects neonatal seizures well, has a good PPV and is suited for cEEG monitoring. Changes in electrographic characteristics such as amplitude, duration and rhythmicity in relation to deteriorating EEG background tend to worsen the performance of automated seizure detection. SIGNIFICANCE: cEEG monitoring is important for detecting seizures in the neonatal intensive care unit (NICU). Our automated algorithm reliably detects neonatal seizures that are likely to be clinically most relevant, as reflected by the associated EEG background abnormality.
Authors: Ivana Despotovic; Perumpillichira J Cherian; Maarten De Vos; Hans Hallez; Wouter Deburchgraeve; Paul Govaert; Maarten Lequin; Gerhard H Visser; Renate M Swarte; Ewout Vansteenkiste; Sabine Van Huffel; Wilfried Philips Journal: Hum Brain Mapp Date: 2012-04-21 Impact factor: 5.038
Authors: Vladimir Matic; Perumpillichira Joseph Cherian; Ninah Koolen; Amir H Ansari; Gunnar Naulaers; Paul Govaert; Sabine Van Huffel; Maarten De Vos; Sampsa Vanhatalo Journal: Front Hum Neurosci Date: 2015-04-23 Impact factor: 3.169
Authors: Sean R Mathieson; Nathan J Stevenson; Evonne Low; William P Marnane; Janet M Rennie; Andrey Temko; Gordon Lightbody; Geraldine B Boylan Journal: Clin Neurophysiol Date: 2015-05-09 Impact factor: 3.708