Predicting term and preterm delivery with transabdominal uterine electromyography

Maner, William L.

doi:10.1016/s0029-7844(03)00341-7

Cited by 137 publications

(100 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, (Maner, 2003) found that uterine electrical activity occurred within 1Hz and that the maternal heart-rate was always higher than 1Hz. Furthermore, 95% of the patients measured had respiration rates of 0.33 Hz or less.…”

Section: Methodsmentioning

confidence: 92%

Advanced artificial neural network classification for detecting preterm births using EHG records

et al. 2016

View full text Add to dashboard Cite

Abstract-Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate.

show abstract

Section: Methodsmentioning

confidence: 92%

Advanced artificial neural network classification for detecting preterm births using EHG records

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The pre-processing technique used in (Leman H, Marque C, 1999) passed the EHG signal through a Butterworth filter configured to filter 0.8-4 Hz, 0.3-4 Hz, and 0.3-3Hz frequencies. However, (Maner, 2003) found that uterine electrical activity occurred within 1Hz and that the maternal heart-rate was always higher than 1Hz. Furthermore, 95% of the patients measured had respiration rates of 0.33 Hz or less.…”

Section: Methodsmentioning

confidence: 92%

Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction Techniques for Detecting Preterm Births Using EHG Records

Fergus

Idowu

Hussain

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate.

show abstract

“…Studies have shown that the analysis of EHG can conduct an independent and easier way to monitor labour evolution [12, 13]. Maner et al [14] in their work determined whether delivery can be predicted using EHG. Moslem et al [15] in their work investigated whether pregnancy can be successfully monitored and labour can be accurately predicted using EHG.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Chen

Hao

2017

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

show abstract

Predicting term and preterm delivery with transabdominal uterine electromyography

Abstract: Transabdominal uterine electromyography predicts delivery within 24 hours at term and within 4 days preterm. This methodology offers many advantages and benefits that are not available with present uterine monitoring systems.

Cited by 137 publications

References 18 publications

Advanced artificial neural network classification for detecting preterm births using EHG records

Advanced artificial neural network classification for detecting preterm births using EHG records

Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction Techniques for Detecting Preterm Births Using EHG Records

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Contact Info

Product

Resources

About