{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T12:26:41Z","timestamp":1725625601297},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007751","name":"AGH University of Science and Technology","doi-asserted-by":"publisher","award":["No. 16.16.120.773"],"id":[{"id":"10.13039\/501100007751","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager\u2013Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.<\/jats:p>","DOI":"10.3390\/s22041507","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T03:44:47Z","timestamp":1644983087000},"page":"1507","source":"Crossref","is-referenced-by-count":11,"title":["Prediction of Preterm Delivery from Unbalanced EHG Database"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6167-5840","authenticated-orcid":false,"given":"Somayeh","family":"Mohammadi Far","sequence":"first","affiliation":[{"name":"AGH University of Science and Technology, 30059 Krakow, Poland"}]},{"given":"Matin","family":"Beiramvand","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful 313, Iran"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6625-7923","authenticated-orcid":false,"given":"Mohammad","family":"Shahbakhti","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5986-3247","authenticated-orcid":false,"given":"Piotr","family":"Augustyniak","sequence":"additional","affiliation":[{"name":"AGH University of Science and Technology, 30059 Krakow, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.siny.2015.12.011","article-title":"The epidemiology, etiology, and costs of preterm birth","volume":"21","author":"Frey","year":"2016","journal-title":"Semin. 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