Automated artifact removal as preprocessing refines neonatal seizure detection

ObjectiveThe description and evaluation of algorithms using Independent Component Analysis (ICA) for automatic removal of ECG, pulsation and respiration artifacts in neonatal EEG before automated seizure detection.MethodsThe developed algorithms decompose the EEG using ICA into its underlying sources. The artifact source was identified using the simultaneously recorded polygraphy signals after preprocessing. The EEG was reconstructed without the corrupting source, leading to a clean EEG. The impact of the artifact removal was measured by comparing the performance of a previously developed seizure detector before and after the artifact removal in 13 selected patients (9 having artifact-contaminated and 4 having artifact-free EEGs).ResultsA significant decrease in false alarms (p = 0.01) was found while the Good Detection Rate (GDR) for seizures was not altered (p = 0.50).ConclusionsThe techniques reduced the number of false positive detections without lowering sensitivity and are beneficial in long term EEG seizure monitoring in the presence of disturbing biological artifacts.SignificanceThe proposed algorithms improve neonatal seizure monitoring.

► The paper discusses an improved algorithm for neonatal seizure detection.
► The paper discusses a novel way for removing artifacts frequently present in the neonatal EEG.
► The paper shows that removing these artifacts prior to seizure detection significantly reduces the number of false alarms.