Wavelet and Over-Segmentation Elimination Techniques Effect on Automatic Uterine EMG Segmentation Performance / Quality

Over-segmentation in non-stationary signals remains a challenge in several automatic segmentation methods. In this context, most of automatic segmentation methods of contractions in multichannel uterine Electromyogram signals (Electrohysterogram signals, EHGs) encounter the problem of detecting other events which are not corresponding to contractions labeled by expert. In addition, the great advantage of using wavelet manifests by its ability to separate the fine details in a signal. Many studies applied wavelets for sake of detecting and analyzing abrupt changes in non-stationary signals. This study is another step of our project focused on the automatic contraction segmentation of EHGs. Indeed, we will focus on the application of dynamic cumulative sum method (DCS) with over-segmentation elimination techniques on bipolar EHGs and details after wavelet decomposition of those signals. Detected events are then compared to contractions identified by expert using Margin validation test with and without application of over-segmentation techniques. Regarding the obtained sensitivity and other events rate of methods we find that DCS with over-segmentation elimination techniques application on bipolar EHGs gives the highest sensitivity 100% and lowest other event rate 39.77%.