Review on EHG signal analysis and its application in preterm diagnosis
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TLDR
In this paper , a review of the application of EHG signal analysis and its application to preterm birth diagnostic methods, and in particular on the analysis of such signals using machine learning techniques is presented.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2022-01-01 and is currently open access. It has received 11 citations till now. The article focuses on the topics: Computer science.read more
Citations
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Prediction of Preterm Delivery from Unbalanced EHG Database
TL;DR: 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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Modeling and experimental approaches for elucidating multi-scale uterine smooth muscle electro- and mechano-physiology: A review
TL;DR: Differences in the underlying physiology between human and common animal models utilized in experiments, and the experimental interventions and computational models used to assess uterine function are investigated are investigated.
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Predicting preterm births from electrohysterogram recordings via deep learning
Uri Goldsztejn,Arye Nehorai +1 more
TL;DR: In this article , a deep learning method was developed to predict preterm births directly from electrohysterogram (EHG) recordings of pregnant mothers without symptoms of preterm labor.
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Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity
Alba Diaz-Martinez,Rogelio Monfort-Ortiz,Yiyao Ye-Lin,Javier Garcia-Casado,Mar Nieto-Tous,Félix Nieto-del-Amor,Vicente José Diago-Almela,Gema Prats-Boluda +7 more
TL;DR: In this article , the authors identify EHG-biomarkers to predict IOL success (active phase of labour in ≤ 24 h) and determine the influence of the myoelectrical response on the parity of this group.
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Estimating uterine activity from electrohysterogram measurements via statistical tensor decomposition
Uri Goldsztejn,Arye Nehorai +1 more
TL;DR: In this paper , a Bayesian tensor decomposition for estimating localized and distributed electrical activities was proposed to distinguish EHG bursts from other interfering activities recorded in EHGs using real measurements from two public datasets.
References
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A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
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SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
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Survey on deep learning with class imbalance
TL;DR: Examination of existing deep learning techniques for addressing class imbalanced data finds that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered.
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MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
TL;DR: A new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems and is better than or comparable with some other existing methods in terms of various assessment metrics.