Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

Prats-Boluda, Gema; Pastor-Tronch, Julio; Garcia-Casado, Javier; Monfort-Ortíz, Rogelio; Marín, Alfredo Perales; Diago, Vicente; Prats, Alba Roca; Ye-Lin, Yiyao

doi:10.3390/s21072496

Cited by 9 publications

(3 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This appeared more robust than earlier methods to automate uterine contraction detection which showed falls in sensitivity and PPV on low quality recordings ( Horoba et al, 2016 ). Recently, algorithms from electrohysterography signals, acquired by electrodes placed on the mother's abdomen are being developed to predict embryo implantation for IVF patients ( Sammali et al, 2021 ) and preterm birth ( Cheng et al, 2022 ; Prats-Boluda et al, 2021 , Nieto-del-Amor et al, 2022 ). One method using novel multichannel entropy features appeared accurate in predicting preterm birth (90.5%) ( Cheng et al, 2022 ).…”

Section: Resultsmentioning

confidence: 99%

Machine learning and disease prediction in obstetrics

Arain

Iliodromiti

Slabaugh

et al. 2023

Current Research in Physiology

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Machine learning and disease prediction in obstetrics

Arain

Iliodromiti

Slabaugh

et al. 2023

Current Research in Physiology

View full text Add to dashboard Cite

“…The FW of the EHG ([0.2-4] Hz) is typically studied in sliding windows of 120s (Mas-Cabo et al, 2020b;Prats-Boluda et al, 2021). As the period of the SW ranges from 33.3s to 200s, a larger analysis window was required, therefore a window length of 500s was selected.…”

Section: Sw Parametrizationmentioning

confidence: 99%

Uterine slow wave: directionality and changes with imminent delivery

Albaladejo-Belmonte¹,

Prats-Boluda²,

Ye-Lin³

et al. 2022

Physiol. Meas.

Self Cite

View full text Add to dashboard Cite

Objective. The slow wave (SW) of the electrohysterogram (EHG) may contain relevant information on the electrophysiological condition of the uterus throughout pregnancy and labor. Our aim was to assess differences in the SW as regards the imminence of labor and the directionality of uterine myoelectrical activity. Approach. The SW of the EHG was extracted from the signals of the Icelandic 16-electrode EHG database in the bandwidth [5, 30] mHz and its power, spectral content, complexity and synchronization between the horizontal (X) and vertical (Y) directions were characterized by the root mean square (RMS), dominant frequency (domF), sample entropy (SampEn) and maximum cross-correlation (CCmax) of the signals, respectively. Significant differences between parameters at time-to-delivery (TTD) ≤7 vs. >7 days and between the horizontal vs. vertical directions were assessed. Main results. The SW power significantly increased in both directions as labor approached (TTD≤7d vs. >7d (mean±SD): x= 0.12±0.10 vs. 0.08±0.06mV; y= 0.12±0.09 vs. 0.08±0.05mV), as well as the dominant frequency in the horizontal direction (= 9.1±1.3 vs. 8.5±1.2mHz) and the synchronization between both directions (= 0.44±0.16 vs. 0.36±0.14). Furthermore, its complexity decreased in the vertical direction (= 6.13·10−2±8.7·10−3 vs. 6.50·10−2±8.3·10−3), suggesting a higher cell-to-cell electrical coupling. Whereas there were no differences between the SW features in both directions in the general population, statistically significant differences were obtained between them in individuals in many cases. Significance. Our results suggest that the SW of the EHG is related to bioelectrical events in the uterus and could provide objective information to clinicians in challenging obstetric scenarios.

show abstract

“…Even in the case of survival, a premature neonate may face several serious complications such as difficulties in breathing and vision problems due to underdeveloped organs [7]. Furthermore, preterm labor may also have adverse influences on maternal well-being due to the mother's perception of her baby [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks

Far

Beiramvand

Shahbakhti

et al. 2023

Sensors

View full text Add to dashboard Cite

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother’s mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks.

show abstract

Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

Cited by 9 publications

References 67 publications

Machine learning and disease prediction in obstetrics

Machine learning and disease prediction in obstetrics

Uterine slow wave: directionality and changes with imminent delivery

Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks

Contact Info

Product

Resources

About