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. Copyright Â
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. Copyright Â
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: Shennan Aibel Weiss; Ali A Asadi-Pooya; Sitaram Vangala; Stephanie Moy; Dale H Wyeth; Iren Orosz; Michael Gibbs; Lara Schrader; Jason Lerner; Christopher K Cheng; Edward Chang; Rajsekar Rajaraman; Inna Keselman; Perdro Churchman; Christine Bower-Baca; Adam L Numis; Michael G Ho; Lekha Rao; Annapoorna Bhat; Joanna Suski; Marjan Asadollahi; Timothy Ambrose; Andres Fernandez; Maromi Nei; Christopher Skidmore; Scott Mintzer; Dawn S Eliashiv; Gary W Mathern; Marc R Nuwer; Michael Sperling; Jerome Engel; John M Stern Journal: F1000Res Date: 2017-01-10
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: A Tsanas; K E A Saunders; A C Bilderbeck; N Palmius; M Osipov; G D Clifford; G Μ Goodwin; M De Vos Journal: J Affect Disord Date: 2016-07-02 Impact factor: 4.839