Literature DB >> 32768044

Uterine contractions clustering based on electrohysterography.

Filipa Esgalhado1, Arnaldo G Batista2, Helena Mouriño3, Sara Russo4, Catarina R Palma Dos Reis5, Fátima Serrano5, Valentina Vassilenko1, Manuel Ortigueira6.   

Abstract

The uterine electromyogram, also named Electrohysterogram (EHG), is a non-invasive technique that has been used for pregnancy and labour monitoring as well as for research work on uterine physiology. This technique is well established in this field. There is however a vast unexplored potential in the EHG that is currently the subject of interdisciplinary research work involving different scientific fields such as medicine, engineering, physics and mathematics. In this paper, an unsupervised clustering method is applied to a previously obtained set of frequency spectral representations of the respective EHG signal contractions that were previously automatically detected and delineated. An innovative approach using the complete spectrum projection is described, rather than a set of relevant points. The feasibility of the method is established despite the concerns of possible computational burden incurred by the processing of the whole spectrum. Given the unsupervised nature of this classification, a validation procedure was performed whereas the obtained clusters were labelled through the correlation with the common knowledge about the most relevant uterine contraction types, as described in the literature. As a result of this study, a spectral description of the Alvarez contractions was obtained where it was possible to breakdown these important events in two different types according to their spectrum. Spectral estimates of Braxton-Hicks contractions were also obtained and associated to one of the clusters. This led to a full spectral characterization of these uterine events.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Keywords:  Clustering; Electrohysterogram; Hierarchical clustering; Pregnancy monitoring; Uterine electromyography

Mesh:

Year:  2020        PMID: 32768044     DOI: 10.1016/j.compbiomed.2020.103897

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  4 in total

Review 1.  Alvarez waves in pregnancy: a comprehensive review.

Authors:  Sara Russo; Arnaldo Batista; Filipa Esgalhado; Catarina R Palma Dos Reis; Fátima Serrano; Valentina Vassilenko; Manuel Ortigueira
Journal:  Biophys Rev       Date:  2021-07-08

2.  Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate.

Authors:  Xiaoxiao Song; Xiangyun Qiao; Dongmei Hao; Lin Yang; Xiya Zhou; Yuhang Xu; Dingchang Zheng
Journal:  Sci Rep       Date:  2021-01-21       Impact factor: 4.379

3.  Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram.

Authors:  Daniela Martins; Arnaldo Batista; Helena Mouriño; Sara Russo; Filipa Esgalhado; Catarina R Palma Dos Reis; Fátima Serrano; Manuel Ortigueira
Journal:  Sensors (Basel)       Date:  2022-10-09       Impact factor: 3.847

4.  A Preliminary Exploration of the Placental Position Influence on Uterine Electromyography Using Fractional Modelling.

Authors:  Müfit Şan; Arnaldo Batista; Sara Russo; Filipa Esgalhado; Catarina R Palma Dos Reis; Fátima Serrano; Manuel Ortigueira
Journal:  Sensors (Basel)       Date:  2022-02-22       Impact factor: 3.576

  4 in total

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