title: Classification of multichannel uterine EMG signals.
abstract: Classification of multichannel uterine electromyogram (EMG) signals is addressed. Signals were recorded by a matrix of 16 electrodes. First, signals corresponding to each channel were individually classified using an artificial neural network (ANN) based on radial basis functions (RBF). The results have shown that the classification performance varies from one channel to another. Then, a decision fusion method based on these classification performances was tested. After fusion, the network yielded better classification accuracy than any individual channel could provide. The high percentage of correctly classified labor/non-labor events proves the efficiency of multichannel recordings in detecting labor. These findings can be very useful for the aim of classifying antepartum versus labor patients.
authors.affiliation: Laboratoire Biomécanique et Bio-ingénierie, University of Technology of Compiègne – CNRS UMR 6600 Compiègne, Cedex, France. bassam.moslem@utc.fr
authors.forename: B
authors.initials: B
authors.lastname: Moslem
authors.forename: M O
authors.initials: MO
authors.lastname: Diab
authors.forename: C
authors.initials: C
authors.lastname: Marque
authors.forename: M
authors.initials: M
authors.lastname: Khalil
mesh_terms: D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D007743:Labor, Obstetric; D016571:Neural Networks, Computer; D011247:Pregnancy; D014599:Uterus / Q000502:physiology*