Automated artifact removal as preprocessing refines neonatal seizure detection

Abstract

Objective: The 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.

Methods: The 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).

Results: A significant decrease in false alarms (p=0.01) was found while the Good Detection Rate (GDR) for seizures was not altered (p=0.50).

Conclusions: The 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.

Significance: The proposed algorithms improve neonatal seizure monitoring.