Automatic segmentation of bipolar EHGs’ contractions using wavelet decomposition - Mono & Multi-dimensional Study

Automatic segmentation of contractions in multichannel uterine EMG signals is the main issue of interest in many recent studies. Most of them are faced with the problem of the detecting other events than contractions. Wavelets have the great advantage of being able 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 contractions segmentation of uterine EMG signals. Indeed, we will focus on the application of dynamic cumulative sum method (DCS) with over-segmentation elimination techniques on details after wavelet decomposition of bipolar uterine EMG records. DCS will be applied in mono- and multi-dimensional. Detected events are then compared to contractions identified by expert using Margin validation test. Regarding the obtained sensitivity and other events rate of methods we find that DCS with multidimensional application give the highest sensitivity 100% and lowest other events rate 49.6%.