Automatic Monodimensional EHG
Contractions’ Segmentation
Amer Zaylaa1, Ahmad Diab2, Mohamad Khalil3, and Catherine Marque1
Abstract: Until recently,
many studies have been achieved for the sake of automatically segmentation of
the Electrohysterogram (EHG) in order to identify the efficient uterine contractions
but the most of them encountered the presence of other events such as motion
artifacts and other kind of contractions despite of the use of efficient
filtering methods. In this study, we apply an online method which is developed
previously and known by Dynamic Cumulative Sum (DCS) on monopolar EHG signals
acquired through a 4x4 electrodes matrix with and without Canonical Correlation
Analysis and Empirical Mode Decomposition (CCA-EMD) denoising method, then on monopolar
EHG after wavelet decomposition. The detected segments are driven through an
automatic concatenation technique of detected event time from all channels in
order to reduce the unwanted segments, the obtained segments then undergo to
implemented Margin validation test in order to classify among them. Sensitivity
of detected contractions and other detected events rate referring to identified
contractions by expert have been calculated in order to track the efficiency of
the fully automated multichannel segmentation method. Additional EHG filtering
techniques like CCA-EMD method seems to be better but effective time cost.
Further studies should be achieved in order to decreasing the other events rate
for the sake of fully identifying the uterine contractions.
Keywords: EHG signal, dynamic
cumulative sum, CCA-EMD denoising method, automatic segmentation, wavelet decomposition,
margin validation test.
Received October 14 2018; accepted January 23 2019