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Shimoga Narayana Rao K, Asha V. An automatic classification approach for preterm delivery detection based on deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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2
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Mohammadi Far S, Beiramvand M, Shahbakhti M, Augustyniak P. Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks. SENSORS (BASEL, SWITZERLAND) 2023; 23:5965. [PMID: 37447815 DOI: 10.3390/s23135965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/15/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023]
Abstract
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.
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Affiliation(s)
| | - Matin Beiramvand
- Faculty of Information Technology and Communication, Tampere University, 33100 Tampere, Finland
| | - Mohammad Shahbakhti
- Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania
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Fischer A, Rietveld A, Teunissen P, Bakker P, Hoogendoorn M. End-to-end learning with interpretation on electrohysterography data to predict preterm birth. Comput Biol Med 2023; 158:106846. [PMID: 37019011 DOI: 10.1016/j.compbiomed.2023.106846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Prediction of preterm birth is a difficult task for clinicians. By examining an electrohysterogram, electrical activity of the uterus that can lead to preterm birth can be detected. Since signals associated with uterine activity are difficult to interpret for clinicians without a background in signal processing, machine learning may be a viable solution. We are the first to employ Deep Learning models, a long-short term memory and temporal convolutional network model, on electrohysterography data using the Term-Preterm Electrohysterogram database. We show that end-to-end learning achieves an AUC score of 0.58, which is comparable to machine learning models that use handcrafted features. Moreover, we evaluate the effect of adding clinical data to the model and conclude that adding the available clinical data to electrohysterography data does not result in a gain in performance. Also, we propose an interpretability framework for time series classification that is well-suited to use in case of limited data, as opposed to existing methods that require large amounts of data. Clinicians with extensive work experience as gynaecologist used our framework to provide insights on how to link our results to clinical practice and stress that in order to decrease the number of false positives, a dataset with patients at high risk of preterm birth should be collected. All code is made publicly available.
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Pirnar Ž, Jager F, Geršak K. Characterization and separation of preterm and term spontaneous, induced, and cesarean EHG records. Comput Biol Med 2022; 151:106238. [PMID: 36343404 DOI: 10.1016/j.compbiomed.2022.106238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 09/30/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
To improve the understanding of the underlying physiological processes that lead to preterm birth, and different term delivery modes, we quantitatively characterized and assessed the separability of the sets of early (23rd week) and later (31st week) recorded, preterm and term spontaneous, induced, cesarean, and induced-cesarean electrohysterogram (EHG) records using several of the most widely used non-linear features extracted from the EHG signals. Linearly modeled temporal trends of the means of the median frequencies (MFs), and of the means of the peak amplitudes (PAs) of the normalized power spectra of the EHG signals, along pregnancy (from early to later recorded records), derived from a variety of frequency bands, revealed that for the preterm group of records, in comparison to all other term delivery groups, the frequency spectrum of the frequency band B0L (0.08-0.3 Hz) shifts toward higher frequencies, and that the spectrum of the newly identified frequency band B0L' (0.125-0.575 Hz), which approximately matches the Fast Wave Low band, becomes stronger. The most promising features to separate between the later preterm group and all other later term delivery groups appear to be MF (p=1.1⋅10-5) in the band B0L of the horizontal signal S3, and PA (p=2.4⋅10-8) in the band B0L' (S3). Moreover, the PA in the band B0L' (S3) showed the highest power to individually separate between the later preterm group and any other later term delivery group. Furthermore, the results suggest that in preterm pregnancies the resting maternal heart rate decreases between the 23rd and 31st week of gestation.
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Affiliation(s)
- Žiga Pirnar
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
| | - Franc Jager
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia.
| | - Ksenija Geršak
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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5
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Martins D, Batista A, Mouriño H, Russo S, Esgalhado F, dos Reis CRP, Serrano F, Ortigueira M. Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram. SENSORS (BASEL, SWITZERLAND) 2022; 22:7638. [PMID: 36236736 PMCID: PMC9571637 DOI: 10.3390/s22197638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be -16.74%, -20.32%, and -15.78%, respectively (p-value = 1.31 × 10-12).
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Affiliation(s)
- Daniela Martins
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Arnaldo Batista
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
- UNINOVA-CTS, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Helena Mouriño
- Faculty of Sciences, Lisbon University, Campo Grande, 1749-016 Lisbon, Portugal
- CEAUL Faculty of Sciences, Lisbon University, Campo Grande, 1749-016 Lisbon, Portugal
| | - Sara Russo
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Filipa Esgalhado
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Catarina R. Palma dos Reis
- Department of Obstetrics, University Central Hospital Lisbon (CHULC), 1169-050 Lisboa, Portugal
- Comprehensive Health Research Centre (CHRC), NOVA Medical School, Universidade NOVA de Lisboa, 1169-056 Lisboa, Portugal
| | - Fátima Serrano
- Department of Obstetrics, University Central Hospital Lisbon (CHULC), 1169-050 Lisboa, Portugal
- Comprehensive Health Research Centre (CHRC), NOVA Medical School, Universidade NOVA de Lisboa, 1169-056 Lisboa, Portugal
| | - Manuel Ortigueira
- NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
- UNINOVA-CTS, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
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Nieto-del-Amor F, Prats-Boluda G, Garcia-Casado J, Diaz-Martinez A, Diago-Almela VJ, Monfort-Ortiz R, Hao D, Ye-Lin Y. Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data. SENSORS 2022; 22:s22145098. [PMID: 35890778 PMCID: PMC9319575 DOI: 10.3390/s22145098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/01/2022] [Accepted: 07/05/2022] [Indexed: 02/01/2023]
Abstract
Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.
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Affiliation(s)
- Félix Nieto-del-Amor
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
- Correspondence:
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
| | - Alba Diaz-Martinez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
| | | | - Rogelio Monfort-Ortiz
- Servicio de Obstetricia, H.U.P. La Fe, 46026 Valencia, Spain; (V.J.D.-A.); (R.M.-O.)
| | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China;
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (J.G.-C.); (A.D.-M.); (Y.Y.-L.)
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Prediction of Preterm Delivery from Unbalanced EHG Database. SENSORS 2022; 22:s22041507. [PMID: 35214412 PMCID: PMC8878555 DOI: 10.3390/s22041507] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/07/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
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–Kaiser 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.
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Nieto-del-Amor F, Beskhani R, Ye-Lin Y, Garcia-Casado J, Diaz-Martinez A, Monfort-Ortiz R, Diago-Almela VJ, Hao D, Prats-Boluda G. Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. SENSORS 2021; 21:s21186071. [PMID: 34577278 PMCID: PMC8471282 DOI: 10.3390/s21186071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/06/2021] [Accepted: 09/08/2021] [Indexed: 11/16/2022]
Abstract
One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.
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Affiliation(s)
- Félix Nieto-del-Amor
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Raja Beskhani
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
- Correspondence:
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Alba Diaz-Martinez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Rogelio Monfort-Ortiz
- Servicio de Obstetricia, H.U.P. La Fe, 46026 Valencia, Spain; (R.M.-O.); (V.J.D.-A.)
| | | | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China;
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
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Preterm-term birth classification using EMD-based time-domain features of single-channel electrohysterogram data. Phys Eng Sci Med 2021; 44:1151-1159. [PMID: 34463948 DOI: 10.1007/s13246-021-01051-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/26/2021] [Indexed: 10/20/2022]
Abstract
Preterm birth anticipation is a crucial task that can reduce both the rate and the complications of preterm birth. Electrohysterogram (EHG) or uterine electromyogram (EMG) data have shown that they can provide useful information for preterm birth anticipation. Four distinct time-domain features (mean absolute value, average amplitude change, difference in absolute standard deviation value, and log detector) that are commonly applied to EMG signal processing were utilized and investigated in this study. A single channel of EHG data was decomposed into its constituent components (i.e., into intrinsic mode functions) by using empirical mode decomposition (EMD) before their time-domain features were extracted. The time-domain features of the intrinsic mode functions of the EHG data associated with preterm and term births were applied for preterm-term birth classification by using a support vector machine with a radial basis function. The preterm-term birth classifications were validated by using 10-fold cross validation. From the computational results, it was shown that excellent preterm-term birth classification can be achieved by using single-channel EHG data. The computational results further suggested that the best overall performance concerning preterm-term birth classification was obtained when thirteen (out of sixteen) EMD-based time-domain features were applied. The best accuracy, sensitivity, specificity, and [Formula: see text]-score achieved were 0.9382, 0.9130, 0.9634, and 0.9366, respectively.
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10
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Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography. SENSORS 2021; 21:s21072496. [PMID: 33916679 PMCID: PMC8038321 DOI: 10.3390/s21072496] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/30/2022]
Abstract
Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.
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Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records. SENSORS 2020; 20:s20247328. [PMID: 33419319 PMCID: PMC7766070 DOI: 10.3390/s20247328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 11/16/2022]
Abstract
The aim of the present study was to assess the capability of conduction velocity amplitudes and directions of propagation of electrohysterogram (EHG) waves to better distinguish between preterm and term EHG surface records. Using short-time cross-correlation between pairs of bipolar EHG signals (upper and lower, left and right), the conduction velocities and their directions were estimated using preterm and term EHG records of the publicly available Term–Preterm EHG DataSet with Tocogram (TPEHGT DS) and for different frequency bands below and above 1.0 Hz, where contractions and the influence of the maternal heart rate on the uterus, respectively, are expected. No significant or preferred continuous direction of propagation was found in any of the non-contraction (dummy) or contraction intervals; however, on average, a significantly lower percentage of velocity vectors was found in the vertical direction, and significantly higher in the horizontal direction, for preterm dummy intervals above 1.0 Hz. The newly defined features—the percentages of velocities in the vertical and horizontal directions, in combination with the sample entropy of the EHG signal recorded in the vertical direction, obtained from dummy intervals above 1.0 Hz—showed the highest classification accuracy of 86.8% (AUC=90.3%) in distinguishing between preterm and term EHG records of the TPEHGT DS.
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12
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Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios. ENTROPY 2020; 22:e22070743. [PMID: 33286515 PMCID: PMC7517284 DOI: 10.3390/e22070743] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/18/2020] [Accepted: 07/03/2020] [Indexed: 12/19/2022]
Abstract
Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel–Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.
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13
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Deep neural network for semi-automatic classification of term and preterm uterine recordings. Artif Intell Med 2020; 105:101861. [PMID: 32505424 DOI: 10.1016/j.artmed.2020.101861] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 02/25/2020] [Accepted: 04/14/2020] [Indexed: 02/04/2023]
Abstract
Pregnancy is a complex process, and the prediction of premature birth is uncertain. Many researchers are exploring non-invasive approaches to enhance its predictability. Currently, the ElectroHysteroGram (EHG) and Tocography (TOCO) signal are a real-time and non-invasive technology which can be employed to predict preterm birth. For this purpose, sparse autoencoder (SAE) based deep neural network (SAE-based DNN) is developed. The deep neural network has three layers including a stacked sparse autoencoder (SSAE) network with two hidden layers and one final softmax layer. To this end, the bursts of all 26 recordings of the publicly available TPEHGT DS database corresponding to uterine contraction intervals and non-contraction intervals (dummy intervals) were manually segmented. 20 features were extracted by two feature extraction algorithms including sample entropy and wavelet entropy. Afterwards, the SSAE network is adopted to learn high-level features from raw features by unsupervised learning. The softmax layer is added at the top of the SSAE network for classification. In order to verify the effectiveness of the proposed method, this study used 10-fold cross-validation and four indicators to evaluate classification performance. Experimental research results display that the performance of deep neural network can achieve Sensitivity of 98.2%, Specificity of 97.74%, and Accuracy of 97.9% in the publicly TPEHGT DS database. The performance of deep neural network outperforms the comparison models including deep belief networks (DBN) and hierarchical extreme learning machine (H-ELM). Finally, experimental research results reveal that the proposed method could be valid applied to semi-automatic identification of term and preterm uterine recordings.
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14
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Mas-Cabo J, Prats-Boluda G, Garcia-Casado J, Alberola-Rubio J, Monfort-Ortiz R, Martinez-Saez C, Perales A, Ye-Lin Y. Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy. SENSORS 2020; 20:s20092681. [PMID: 32397177 PMCID: PMC7248811 DOI: 10.3390/s20092681] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 04/24/2020] [Accepted: 05/07/2020] [Indexed: 12/22/2022]
Abstract
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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Affiliation(s)
- J Mas-Cabo
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - G Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - J Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | | | - R Monfort-Ortiz
- Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain
| | - C Martinez-Saez
- Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain
| | - A Perales
- Servicio de Obstetricia, H.U. P. La Fe, 46026 Valencia, Spain
| | - Y Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
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15
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Degbedzui DK, Yüksel ME. Accurate diagnosis of term-preterm births by spectral analysis of electrohysterography signals. Comput Biol Med 2020; 119:103677. [PMID: 32339119 DOI: 10.1016/j.compbiomed.2020.103677] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/23/2020] [Accepted: 02/23/2020] [Indexed: 12/20/2022]
Abstract
Preterm delivery contributes to an increased risk of fetal and maternal death as well as several health deficiencies, thereby requiring special care and treatment that result in high financial costs. It is therefore of key importance to diagnose preterm delivery in advance in order to avoid or minimize its undesirable consequences. This paper proposes a novel method for non-invasive diagnosis of preterm delivery based on the classification of electrohysterography (EHG) signals. First, the EHG signal, which is related to the electrical activity of uterine muscles is recorded from the maternal fundus using surface electrodes. Then, the signal is sliced into frames for spectral analysis. Next, spectral analyses of the individual EHG signal frames are carried out and centroid frequencies of the frames are computed, establishing the elements of a feature vector that represents the time-varying spectral content of the EHG signal. Finally, this feature vector is employed for the classification of the underlying EHG signal for term-preterm diagnosis. The efficiency of the proposed approach is evaluated and compared with representative methods from the literature. Our results demonstrate that the proposed approach exhibits superior performance over other methods.
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Affiliation(s)
- Derek Kweku Degbedzui
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri, 38039, Turkey.
| | - Mehmet Emin Yüksel
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri, 38039, Turkey.
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16
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Peng J, Hao D, Yang L, Du M, Song X, Jiang H, Zhang Y, Zheng D. Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest. Biocybern Biomed Eng 2020; 40:352-362. [PMID: 32308250 PMCID: PMC7153772 DOI: 10.1016/j.bbe.2019.12.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26th week of gestation.
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Key Words
- ACC, accuracy
- ADASYN, adaptive synthetic sampling approach
- ANN, artificial neural network
- AR, auto-regressive model
- AUC, the area under the curve
- CorrDim, correlation dimension
- DT, decision tree
- EHG, electrohysterogram
- Electrohysterogram (EHG)
- Feature extraction
- Gestational week
- IUPC, intrauterine pressure catheter
- K-NN, K-nearest
- LDA, linear discriminant analysis
- LE, Lyapunov exponent
- MDF, median frequency
- MNF, mean frequency
- PE, preterm delivery before the 26th week of gestation
- PF, peak frequency
- PL, preterm delivery after the 26th week of gestation
- Preterm delivery
- QDA, quadratic discriminant analysis
- RF, random forest
- RMS, root mean square
- ROC, the receiver operating characteristic curve
- Random forest (RF).
- SD, standard deviation
- SE, energy values in signal
- SM, maximum values in signal
- SS, singular values in signal
- SV, variance values in signal
- SVM, support vector machine
- SampEn, sample entropy
- TE, term delivery before the 26th week of gestation
- TL, term delivery after the 26th week of gestation
- TOCO, tocodynamometer
- TPEHG, term-preterm electrohysterogram
- Tr, time reversibility
- τz, zero-crossing
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Affiliation(s)
- Jin Peng
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Dongmei Hao
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Lin Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Mengqing Du
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Xiaoxiao Song
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Hongqing Jiang
- Beijing Haidian Maternal and Children Health Hospital, Beijing, China
| | - Yunhan Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, China
| | - Dingchang Zheng
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, UK
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17
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Saleem S, Saeed A, Usman S, Ferzund J, Arshad J, Mirza J, Manzoor T. Granger causal analysis of electrohysterographic and tocographic recordings for classification of term vs. preterm births. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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18
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Tylcz JB, Muszynski C, Dauchet J, Istrate D, Marque C. An Automatic Method for the Segmentation and Classification of Imminent Labor Contraction From Electrohysterograms. IEEE Trans Biomed Eng 2019; 67:1133-1141. [PMID: 31352329 DOI: 10.1109/tbme.2019.2930618] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Preterm birth is the first cause of perinatal morbidity and mortality. Despite continuous clinical routine improvements, the preterm rate remains steady. Moreover, the specificity of the early diagnosis stays poor as many hospitalized women for preterm delivery threat finally deliver at term. In this context, the use of electrohysterograms may increase the sensitivity and the specificity of early diagnosis of preterm labor. METHODS This paper proposes a clinical application of electrohysterogram processing for the classification of patients as prone to deliver within a week or later. The approach relies on non-linear correlation analysis for the contraction bursts extraction and uses computation of various features combined with the use of Gaussian mixture models for their classification. The method is tested on a new dataset of 68 records collected on women hospitalized for preterm delivery threat. RESULTS This paper presents promising results for the automatic segmentation of the contraction and a classification sensitivity, specificity, and accuracy of, respectively, 80.7%, 76.3%, and 76.2%. CONCLUSION These results are in accordance with the gold standards but have the advantage to be non-invasive and could be performed at home. SIGNIFICANCE Diagnosis of imminent labor is possible by electrohysterography recording and may help in avoiding over-medication and in providing better cares to at-risk pregnant women.
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Hao D, Qiu Q, Zhou X, An Y, Peng J, Yang L, Zheng D. Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions. Biocybern Biomed Eng 2019; 39:806-813. [PMID: 31787794 PMCID: PMC6876647 DOI: 10.1016/j.bbe.2019.06.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 11/18/2022]
Abstract
The aims of this study were to apply decision tree to classify uterine activities (contractions and non-contractions) using the waveform characteristics derived from different channels of electrohysterogram (EHG) signals and then rank the importance of these characteristics. Both the tocodynamometer (TOCO) and 8-channel EHG signals were simultaneously recorded from 34 healthy pregnant women within 24 h before delivery. After preprocessing of EHG signals, EHG segments corresponding to the uterine contractions and non-contractions were manually extracted from both original and normalized EHG signals according to the TOCO signals and the human marks. 24 waveform characteristics of the EHG segments were derived separately from each channel to train the decision tree and classify the uterine activities. The results showed the Power and sample entropy (SamEn) extracted from the un-normalized EHG segments played the most important roles in recognizing uterine activities. In addition, the EHG signal characteristics from channel 1 produced better classification results (AUC = 0.75, Sensitivity = 0.84, Specificity = 0.78, Accuracy = 0.81) than the others. In conclusion, decision tree could be used to classify the uterine activities, and the Power and SamEn of un-normalized EHG segments were the most important characteristics in uterine contraction classification.
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Affiliation(s)
- Dongmei Hao
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Qian Qiu
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Xiya Zhou
- Department of Obstetrics, Peking Union Medical College Hospital, Beijing, China
| | - Yang An
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Jin Peng
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Lin Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Dingchang Zheng
- Health and Wellbeing Academy, Faculty of Medical Science, Anglia Ruskin University, Chelmsford, United Kingdom
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20
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Punitha N, Ramakrishnan S. Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions. Proc Inst Mech Eng H 2019; 233:362-371. [PMID: 30706756 DOI: 10.1177/0954411919827323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.
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Affiliation(s)
- N Punitha
- Non-Invasive Imaging and Diagnostic (NIID) Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - S Ramakrishnan
- Non-Invasive Imaging and Diagnostic (NIID) Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
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21
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A Critical Look at Studies Applying Over-Sampling on the TPEHGDB Dataset. Artif Intell Med 2019. [DOI: 10.1007/978-3-030-21642-9_45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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22
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Mas-Cabo J, Ye-Lin Y, Garcia-Casado J, Alberola-Rubio J, Perales A, Prats-Boluda G. Uterine contractile efficiency indexes for labor prediction: A bivariate approach from multichannel electrohysterographic records. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.07.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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23
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Jager F, Libenšek S, Geršak K. Characterization and automatic classification of preterm and term uterine records. PLoS One 2018; 13:e0202125. [PMID: 30153264 PMCID: PMC6112643 DOI: 10.1371/journal.pone.0202125] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Accepted: 07/09/2018] [Indexed: 11/19/2022] Open
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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Affiliation(s)
- Franc Jager
- Department of Software, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Sonja Libenšek
- Department of Software, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Ksenija Geršak
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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24
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Shahrdad M, Amirani MC. Detection of preterm labor by partitioning and clustering the EHG signal. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.044] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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25
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Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Med Eng Phys 2018; 56:27-35. [DOI: 10.1016/j.medengphy.2018.04.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 03/21/2018] [Accepted: 04/10/2018] [Indexed: 11/19/2022]
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26
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Garcia-Casado J, Ye-Lin Y, Prats-Boluda G, Mas-Cabo J, Alberola-Rubio J, Perales A. Electrohysterography in the diagnosis of preterm birth: a review. Physiol Meas 2018; 39:02TR01. [PMID: 29406317 DOI: 10.1088/1361-6579/aaad56] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries. OBJECTIVE A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context. APPROACH This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization. MAIN RESULTS Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results. SIGNIFICANCE This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.
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Affiliation(s)
- J Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Camino de Vera SN, 46022, Valencia, Spain
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27
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Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7040366] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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28
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A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis. ENTROPY 2016. [DOI: 10.3390/e19010002] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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29
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Ren P, Yao S, Li J, Valdes-Sosa PA, Kendrick KM. Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLoS One 2015; 10:e0132116. [PMID: 26161639 PMCID: PMC4498691 DOI: 10.1371/journal.pone.0132116] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 05/04/2015] [Indexed: 01/28/2023] Open
Abstract
Preterm delivery increases the risk of infant mortality and morbidity, and therefore developing reliable methods for predicting its likelihood are of great importance. Previous work using uterine electromyography (EMG) recordings has shown that they may provide a promising and objective way for predicting risk of preterm delivery. However, to date attempts at utilizing computational approaches to achieve sufficient predictive confidence, in terms of area under the curve (AUC) values, have not achieved the high discrimination accuracy that a clinical application requires. In our study, we propose a new analytical approach for assessing the risk of preterm delivery using EMG recordings which firstly employs Empirical Mode Decomposition (EMD) to obtain their Intrinsic Mode Functions (IMF). Next, the entropy values of both instantaneous amplitude and instantaneous frequency of the first ten IMF components are computed in order to derive ratios of these two distinct components as features. Discrimination accuracy of this approach compared to those proposed previously was then calculated using six differently representative classifiers. Finally, three different electrode positions were analyzed for their prediction accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in prediction accuracy of preterm delivery risk compared with previous approaches, achieving an impressive maximum AUC value of 0.986 when using signals from an electrode positioned below the navel. In sum, this provides a promising new method for analyzing uterine EMG signals to permit accurate clinical assessment of preterm delivery risk.
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Affiliation(s)
- Peng Ren
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
- * E-mail: (PR); (KMM)
| | - Shuxia Yao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingxuan Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
| | - Pedro A. Valdes-Sosa
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M. Kendrick
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
- * E-mail: (PR); (KMM)
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