Classification performance of the frequency-related parameters derived from uterine EMG signals

Moslem, Bassam; Karlsson, B.; Diab, Mohamad O.; Khalil, Mohamad; Marque, Catherine

doi:10.1109/iembs.2011.6090913

Cited by 28 publications

(9 citation statements)

References 17 publications

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“…Most studies, including [10], use four electrodes although one study utilizes two [12]. In a series of other studies, sixteen electrodes were used [13][14][15][16][17][18], and a high density grid of 64 small electrodes was used in [19]. The results show that EHG may vary from women to women.…”

Section: Electrohysterographymentioning

confidence: 98%

“…However, for analysis and feature extraction purposes, translation, into other domains, is possible. These include a frequency representation, via Fourier Transform, [16], [29][30][31] and wavelet transform [25], [28], [31][32][33][34]. The advantage of frequency-related parameters is that they are less susceptible to signal quality variations, due to electrode placement or the physical characteristics of the subjects [27].…”

Section: Feature Extraction From Electrohysterography Signalsmentioning

confidence: 99%

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Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

et al. 2015

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There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography -EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organised network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. the proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%.

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Section: Electrohysterographymentioning

confidence: 98%

Section: Feature Extraction From Electrohysterography Signalsmentioning

confidence: 99%

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

et al. 2015

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“…The k-NN algorithm has been used by Diab et al [15] with an emphasis on Autoregressive (AR) modelling and wavelet transformation pre-processing techniques. The study focused on classifying contractions into three types using data obtained from 16 women.…”

Section: Related Studiesmentioning

confidence: 99%

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Idowu

Fergus

Hussain

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error.

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“…These are spaced at a horizontal, or vertical, distance of 2.5cm to 7cm apart. Most studies use four electrodes, although other configurations have been reported; two (Doret, 2005); 16 (Moslem et al, 2011a(Moslem et al, , 2011b(Moslem et al, , 2011c(Moslem et al, , 2011d2012); and 64 (Rabotti et al, 2010).…”

Section: Electrohysterographymentioning

confidence: 96%

A machine learning system for automatic detection of preterm activity using artificial neural networks and uterine electromyography data

Fergus

Hussain

Al‐Jumeily

et al. 2015

IJAIS

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Background: Preterm births are babies that are born before 37 weeks of gestation. The premature delivery of babies is regarded as a major global public health issue with those affected at greater risk of developing short and long-term complications. The care provided for premature infants has significantly improved. However, it has had no impact on reducing the prevalence of preterm birth. Therefore, a better understanding of why preterm births occur is needed. Methods: Electromyography is used to capture electrical activity in the uterus to help treat and understand the condition, which is time consuming and expensive. This has led to a recent interest in automated detection of the electromyography correlates of preterm activity. This paper explores this idea further using artificial neural networks to classify term and preterm records, using an open dataset containing 300 records of uterine electromyography signals. The Synthetic Minority Oversampling TEchnique is used to oversample the minority preterm class (38 records) to address the issues found in unbalanced datasets and classification. Results: Our approach shows an improvement on existing studies with 94.56% for sensitivity, 87.83% for specificity, and 94% for the area under the curve with 9% global error when using the Multilayer perceptron neural network trained using the Levenberg-Marquardt algorithm. Discussion: The Multilayer perceptron neural network trained using the Levenberg-Marquardt algorithm produced the best results, which is trained using Newton's method of least squares optimization and is an efficient learning algorithm for neural networks that have a few hundred weights, despite being computationally expensive.

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Classification performance of the frequency-related parameters derived from uterine EMG signals

Cited by 28 publications

References 17 publications

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

A machine learning system for automatic detection of preterm activity using artificial neural networks and uterine electromyography data

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