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Multichannel EHG segmentation for automatically identifying contractions and motion artifacts

Abstract : In this study , we have focused on the automatic segmentation of events in the uterine EMG signal and then on the identification of contractions among these events by referring to the expert's knowledge. Our database includes uterine EMG signals of different weeks of gestation acquired through a matrix of 4x4 electrodes. Therefore, our work has first included an application of the dynamic cumulative sum (DCS) method in a monodimensional approach on monopolar signals in order to obtain a high spatial resolution of the data. Based on the obtained results, our study has then focused on bipolar signals in order to increase the signal-to-noise ratio (SNR) of uterine EMGs. In fact, the DCS method has continued by associating first a series of techniques for the elimination of false detected ruptures either based on Fisher or on the SNR and by developing secondly two fusion methods of these ruptures : the firts one is automatic while the other one is based on the weighted majority voting system, where each channel is weighted by a factor when merging the instants of detected ruptures. In addition, the DCS method is applied in a multidimensional approach, first on the bipolar signals, then on their details after wavelet decomposition. Infact, we were interested in the dynamic selection of these details in both approaches by using a technique based on the Kullback Leibler ditance. Finally, in order to indentify the contractions and reduce the number of other detected events, an assay of parameters extraction of these obtained events has been presented and validated.
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Amer Zaylaa. Multichannel EHG segmentation for automatically identifying contractions and motion artifacts. Human health and pathology. Université de Technologie de Compiègne, 2019. English. ⟨NNT : 2019COMP2521⟩. ⟨tel-02522288⟩

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