title: Evaluation of automatic feature detection algorithms in EEG: application to interburst intervals.
abstract: In this paper, we present a new method to compare and improve algorithms for feature detection in neonatal EEG. The method is based on the algorithm׳s ability to compute accurate statistics to predict the results of EEG visual analysis. This method is implemented inside a Java software called EEGDiag, as part of an e-health Web portal dedicated to neonatal EEG. EEGDiag encapsulates a component-based implementation of the detection algorithms called analyzers. Each analyzer is defined by a list of modules executed sequentially. As the libraries of modules are intended to be enriched by its users, we developed a process to evaluate the performance of new modules and analyzers using a database of expertized and categorized EEGs. The evaluation is based on the Davies-Bouldin index (DBI) which measures the quality of cluster separation, so that it will ease the building of classifiers on risk categories. For the first application we tested this method on the detection of interburst intervals (IBI) using a database of 394 EEG acquired on premature newborns. We have defined a class of IBI detectors based on a threshold of the standard deviation on contiguous short time windows, inspired by previous work. Then we determine which detector and what threshold values are the best regarding DBI, as well as the robustness of this choice. This method allows us to make counter-intuitive choices, such as removing the 50 Hz filter (power supply) to save time.
authors.affiliation: LARIS EA7315, L׳UNAM Université, Université Catholique de l׳Ouest, 3 place André-Leroy BP 10808, 49008 Angers, France. Electronic address: pierre.chauvet@uco.fr.
authors.forename: Pierre E
authors.initials: PE
authors.lastname: Chauvet
authors.affiliation: LARIS EA7315, L׳UNAM Université, Centre Hospitalier Universitaire, 4 rue Larrey, 49000 Angers, France.
authors.forename: Sylvie Nguyen The
authors.initials: SN
authors.lastname: Tich
authors.affiliation: ESEO, 10 Boulevard Jean Jeanneteau, 49100 Angers, France.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schang
authors.affiliation: LARIS EA7315, L׳UNAM Université, Institut Universitaire de Technologie, 4 Boulevard Lavoisier BP 42018, Angers cedex, France.
authors.forename: Alain
authors.initials: A
authors.lastname: Clément
mesh_terms: D000465:Algorithms; D001927:Brain Diseases / Q000175:diagnosis* / Q000196:embryology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011296:Prenatal Diagnosis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
keywords: Cluster Validity Index; Clustering; EEG; Interburst intervals; Signal analysis