Machine learning for classification of uterine activity outside pregnancy

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Abstract

The objective of this study was to investigate the use of classification methods by a machine-learning approach for discriminating the uterine activity during the four phases of the menstrual cycle. Four different classifiers, including support vector machine (SVM), K-nearest neighbors (KNN), Gaussian mixture model (GMM) and naïve Bayes are here proposed. A set of amplitude- and frequency-features were extracted from signals measured by two different quantitative and noninvasive methods, such as electrohysterography and ultrasound speckle tracking. The proposed classifiers were trained using all possible feature combinations. The method was applied on a database (24 measurements) collected in different phases of the menstrual cycle, comprising uterine active and quiescent phases. The SVM classifier showed the best performance for discrimination between the different menstrual phases. The classification accuracy, sensitivity, and specificity were 90%, 79%, 93%, respectively. Similar methods can in the future contribute to the diagnosis of infertility or other common uterine diseases such as endometriosis.

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2161-2164
Number of pages4
ISBN (Electronic)978-1-5386-1311-5
ISBN (Print)978-1-5386-1312-2
DOIs
Publication statusPublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period23/07/1927/07/19

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    Bakkes, T. H. G. F., Sammali, F., Kuijsters, N. P. M., Turco, S., Rabotti, C., Schoot, D., & Mischi, M. (2019). Machine learning for classification of uterine activity outside pregnancy. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 2161-2164). [8857374] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2019.8857374