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      Characterization and automatic classification of preterm and term uterine records

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      PLoS ONE
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          Abstract

          Predicting preterm birth is uncertain, and numerous scientists are searching for non-invasive methods to improve its predictability. Current researches are based on the analysis of ElectroHysteroGram (EHG) records, which contain information about the electrophysiological properties of the uterine muscle and uterine contractions. Since pregnancy is a long process, we decided to also characterize, for the first time, non-contraction intervals (dummy intervals) of the uterine records, i.e., EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal). For this purpose, we developed a new set of uterine records, TPEHGT DS, containing preterm and term uterine records of pregnant women, and uterine records of non-pregnant women. We quantitatively characterized contraction intervals (contractions) and dummy intervals of the uterine records of the TPEHGT DS in terms of the normalized power spectra of the EHG and TOCO signals, and developed a new method for predicting preterm birth. The results on the characterization revealed that the peak amplitudes of the normalized power spectra of the EHG and TOCO signals of the contraction and dummy intervals in the frequency band 1.0-2.2 Hz, describing the electrical and mechanical activity of the uterus due to the maternal heart (maternal heart rate), are high only during term pregnancies, when the delivery is still far away; and they are low when the delivery is close. However, these peak amplitudes are also low during preterm pregnancies, when the delivery is still supposed to be far away (thus suggesting the danger of preterm birth); and they are also low or barely present for non-pregnant women. We propose the values of the peak amplitudes of the normalized power spectra due to the influence of the maternal heart, in an electro-mechanical sense, in the frequency band 1.0-2.2 Hz as a new biophysical marker for the preliminary, or early, assessment of the danger of preterm birth. The classification of preterm and term, contraction and dummy intervals of the TPEHGT DS, for the task of the automatic prediction of preterm birth, using sample entropy, the median frequency of the power spectra, and the peak amplitude of the normalized power spectra, revealed that the dummy intervals provide quite comparable and slightly higher classification performances than these features obtained from the contraction intervals. This result suggests a novel and simple clinical technique, not necessarily to seek contraction intervals but using the dummy intervals, for the early assessment of the danger of preterm birth. Using the publicly available TPEHG DB database to predict preterm birth in terms of classifying between preterm and term EHG records, the proposed method outperformed all currently existing methods. The achieved classification accuracy was 100% for early records, recorded around the 23rd week of pregnancy; and 96.33%, the area under the curve of 99.44%, for all records of the database. Since the proposed method is capable of using the dummy intervals with high classification accuracy, it is also suitable for clinical use very early during pregnancy, around the 23rd week of pregnancy, when contractions may or may not be present.

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          Multiscale entropy analysis of complex physiologic time series.

          There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
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            • Record: found
            • Abstract: not found
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            The Divergence and Bhattacharyya Distance Measures in Signal Selection

            T Kailath (1967)
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              SMOTE: Synthetic Minority Over-sampling Technique

              An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: ResourcesRole: SoftwareRole: Visualization
                Role: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Validation
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2018
                28 August 2018
                : 13
                : 8
                : e0202125
                Affiliations
                [1 ] Department of Software, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
                [2 ] Department of Obstetrics and Gynecology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
                The University of Warwick, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5442-0209
                Article
                PONE-D-17-45366
                10.1371/journal.pone.0202125
                6112643
                30153264
                185285eb-e08a-4a41-9a01-80e40983117b
                © 2018 Jager et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 31 December 2017
                : 9 July 2018
                Page count
                Figures: 22, Tables: 11, Pages: 49
                Funding
                Funded by: Slovenian Research Agency (ARRS) (https://www.arrs.gov.si/)
                Award ID: P3-0124 - Metabolic and inborn factors of reproductive health, birth II
                Award Recipient :
                This work was financed by the Slovenian Research Agency (ARRS) ( https://www.arrs.gov.si/) under the research project P3-0124 Metabolic and inborn factors of reproductive health, birth II (FJ, KG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Birth
                Preterm Birth
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Birth
                Preterm Birth
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Pregnancy Complications
                Preterm Birth
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Pregnancy Complications
                Preterm Birth
                Biology and Life Sciences
                Anatomy
                Reproductive System
                Uterus
                Medicine and Health Sciences
                Anatomy
                Reproductive System
                Uterus
                Physical Sciences
                Physics
                Thermodynamics
                Entropy
                Biology and Life Sciences
                Cell Biology
                Signal Transduction
                Cell Signaling
                Calcium Signaling
                Engineering and Technology
                Signal Processing
                Signal Filtering
                Medicine and Health Sciences
                Cardiology
                Heart Rate
                Biology and Life Sciences
                Anatomy
                Reproductive System
                Genital Anatomy
                Cervix
                Medicine and Health Sciences
                Anatomy
                Reproductive System
                Genital Anatomy
                Cervix
                Custom metadata
                All TPEHG DB files are available from the Term-Preterm EHG Database (TPEHG DB) residing on Physionet ( https://physionet.org/physiobank/database/tpehgdb/), doi: 10.13026/C2FW2V. All TPEHGT DS files are available from the Term-Preterm EHG with Tocogram Dataset (TPEHGT DS) residing on Physionet ( https://physionet.org/physiobank/database/tpehgt/), doi: 10.13026/C2166R.

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