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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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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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Diaz-Martinez A, Monfort-Ortiz R, Ye-Lin Y, Garcia-Casado J, Nieto-Tous M, Nieto-Del-Amor F, Diago-Almela V, Prats-Boluda G. Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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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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Yang L, Heiselman C, Quirk JG, Djurić PM. IDENTIFICATION OF UTERINE CONTRACTIONS BY AN ENSEMBLE OF GAUSSIAN PROCESSES. PROCEEDINGS OF THE ... IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING. ICASSP (CONFERENCE) 2021; 2021:10.1109/icassp39728.2021.9414041. [PMID: 34712103 PMCID: PMC8547336 DOI: 10.1109/icassp39728.2021.9414041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uterine activities. We propose a complete four-step method where we address the imbalanced classification problem with an ensemble Gaussian process classifier, where the Gaussian process latent variable model is used as a decision-maker. The results of both simulation and real data show promising performance compared to existing methods.
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Affiliation(s)
- Liu Yang
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Cassandra Heiselman
- Department of Obstetrics, Gynecology and Reproductive Medicine, Stony Brook, NY 11794, USA
| | - J Gerald Quirk
- Department of Obstetrics, Gynecology and Reproductive Medicine, Stony Brook, NY 11794, USA
| | - Petar M Djurić
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
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Song X, Qiao X, Hao D, Yang L, Zhou X, Xu Y, Zheng D. Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate. Sci Rep 2021; 11:1956. [PMID: 33479344 PMCID: PMC7820321 DOI: 10.1038/s41598-021-81492-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/14/2020] [Indexed: 11/09/2022] Open
Abstract
Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs.
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Affiliation(s)
- Xiaoxiao Song
- 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
| | - Xiangyun Qiao
- 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
| | - 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.
| | - Lin Yang
- 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
| | - Xiya Zhou
- Department of Obstetrics, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Yuhang Xu
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Priory Street, Coventry, CV1 5FB, UK
| | - Dingchang Zheng
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Priory Street, Coventry, CV1 5FB, UK
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Vinothini S, Punitha N, Karthick P, Ramakrishnan S. Automated detection of preterm condition using uterine electromyography based topological features. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Xu Y, Liu H, Hao D, Taggart M, Zheng D. Uterus Modeling from Cell to Organ Level: towards Better Understanding of Physiological Basis of Uterine Activity. IEEE Rev Biomed Eng 2020; 15:341-353. [PMID: 32915747 DOI: 10.1109/rbme.2020.3023535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The relatively limited understanding of the physiology of uterine activation prevents us from achieving optimal clinical outcomes for managing serious pregnancy disorders such as preterm birth or uterine dystocia. There is increasing awareness that multi-scale computational modeling of the uterus is a promising approach for providing a qualitative and quantitative description of uterine physiology. The overarching objective of such approach is to coalesce previously fragmentary information into a predictive and testable model of uterine activity that, in turn, informs the development of new diagnostic and therapeutic approaches to these pressing clinical problems. This article assesses current progress towards this goal. We summarize the electrophysiological basis of uterine activation as presently understood and review recent research approaches to uterine modeling at different scales from single cell to tissue, whole organ and organism with particular focus on transformative data in the last decade. We describe the positives and limitations of these approaches, thereby identifying key gaps in our knowledge on which to focus, in parallel, future computational and biological research efforts.
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R P, S SD. Acquisition and Analysis of Electrohysterogram Signal. J Med Syst 2020; 44:66. [PMID: 32040634 DOI: 10.1007/s10916-020-1523-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 01/16/2020] [Indexed: 11/30/2022]
Abstract
Electrohysterogram (EHG) signal is the signal related to action potentials propagating through smooth muscle cells of the uterus (myometrium) to the abdomen of pregnant women which is also known as uterine contraction signal. Cardiotocography (CTG) is the most common method used for monitoring fetal heart rate (FHR) and uterine contractions during pregnancy and labor. This method detects mechanical activity of fetal heart and uterus, however, it provides low accuracy and sensibility and hence more accurate methods are required. The abdominal electrode method of FECG monitoring and Electrohysterography (EHG) are alternative noninvasive method to monitor the FHR and uterine contractions during pregnancy which provides better results compared to CTG. Each information such as the frequency of uterine contractions, length of the contraction and contraction power of uterus, indicates the condition of the uterus which will help the obstetricians to identify the progress of labor. All these above mentioned parameters can be identified from the EHG signal acquired non-invasively by placing the electrodes on the abdomen of the pregnant women. In this work the acquisition of EHG signal as well as analysis of EHG signal in both antepartum condition and labor condition have been carried out and parameters such as number of contractions, contraction duration, amplitude, power of contraction are computed and the quantitative analysis of EHG signals in both above mentioned conditions are performed and it is compared with the simultaneously recorded uterine contraction signal parameters from Cardiotocography (CTG).
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Affiliation(s)
- Parameshwari R
- Department of ECE, College of Engineering Guindy Anna University, Chennai, India.
| | - Shenbaga Devi S
- Department of ECE, College of Engineering Guindy Anna University, Chennai, India
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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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