Identification of preterm birth based on RQA analysis of electrohysterograms

Borowska, Marta; Brzozowska, Ewelina; Ku, Pawe; Oczeretko, Edward; Mosdorf, Romuald; Laudaski, Piotr

doi:10.1016/j.cmpb.2017.10.018

Cited by 24 publications

(15 citation statements)

References 54 publications

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“…In classifying between preterm nonlabor (38 patients), preterm labor (13 patients), term nonlabor (59 patients), and term labor (75 patients) groups (using peak frequency of the power spectrum up to 1.0 Hz, burst duration, number of bursts per unit time, total burst activity, and artificial neural network), the reported percentages of correct classifications were 71%, 92%, 86%, and 79%, respectively [ 18 ]. A recent study on evaluating the applicability of the EHG records for the early detection and classification of preterm and term birth during pregnancy included 20 women between the 24th and 28th week of pregnancy with threatened preterm labor [ 56 ]. The women were divided into two groups: those delivering within seven days and those delivering after more than seven days.…”

Section: Discussionmentioning

confidence: 99%

Characterization and automatic classification of preterm and term uterine records

2018

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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 ...

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Section: Discussionmentioning

confidence: 99%

Characterization and automatic classification of preterm and term uterine records

2018

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“…Various studies have focused on developing tools and algorithms for predicting labor and/or preterm labor using EHG with very promising results, achieving a predictive model accuracy of over 90% or even 95% [19][20][21][22][23][24][25], although none has had a significant impact on clinical practice, probably because the EHG recordings were conducted on women during regular clinical checkups under physiological conditions. In clinical practice, as tocolytic drugs are usually administered to inhibit uterine contractions at the first sign of threatened preterm labor, EHG recordings may be carried out during different tocolytic therapy phases and produce confusing results [26].…”

Section: Introductionmentioning

confidence: 99%

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

Mas-Cabo

Prats-Boluda

Garcia-Casado

et al. 2020

Sensors

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Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 ± 4.3% and 76.2 ± 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.

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“…In classifying between preterm nonlabor (38 patients), preterm labor (13 922 patients), term nonlabor (59 patients), and term labor (75 patients) groups (using peak 923 frequency of the power spectrum up to 1.0 Hz, burst duration, number of bursts per 924 unit time, total burst activity, and artificial neural network), the reported percentages 925 of correct classifications were 71%, 92%, 86%, and 79%, respectively [18]. A recent 926 study on evaluating the applicability of the EHG records for the early detection and 927 classification of preterm and term birth during pregnancy included 20 women between 928 the 24th and 28th week of pregnancy with threatened preterm labor [56]. The women 929 were divided into two groups: those delivering within seven days and those delivering 930 after more than seven days.…”

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Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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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 1/50 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 cap...

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Identification of preterm birth based on RQA analysis of electrohysterograms

Cited by 24 publications

References 54 publications

Characterization and automatic classification of preterm and term uterine records

Characterization and automatic classification of preterm and term uterine records

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

Characterization and automatic classification of preterm and term uterine records

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