Classification of multichannel uterine EMG signals by using a weighted majority voting decision fusion rule

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

doi:10.1109/melcon.2012.6196442

Cited by 6 publications

(8 citation statements)

References 14 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%

“…This was in contrast to the entire thirty coefficients, whose accuracy was 53.11% (±10.5), and sample entropy, which was51.67% (±14.6). In addition, Support Vector Machine (SVM) classification has been used in [13][14][15]. They classify contractions into labour or non-labour using different locations on the abdomen.…”

Section: Electroencephalogram (Eeg) Signals Using Multi-way Array Decmentioning

confidence: 99%

“…The features that were used include the power of the EMG signal, and the median frequency. The highest accuracy for a single SVM classifier, at one location, was 78.4% [13], [14], whilst the overall classification accuracy, for the combined SVM, was 88.4% [15]. Finding the coefficients, for the decision boundary, occurs by solving a quadratic optimisation problem.…”

Section: Electroencephalogram (Eeg) Signals Using Multi-way Array Decmentioning

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: Electroencephalogram (Eeg) Signals Using Multi-way Array Decmentioning

confidence: 99%

Section: Electroencephalogram (Eeg) Signals Using Multi-way Array Decmentioning

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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“…The feature vectors include the power of the EMG signal, and the median frequency. The highest accuracy for a single SVM classifier, at one particular location on the abdomen, was 78.4% (Moslem et al, 2011a(Moslem et al, , 2012, whilst the overall classification accuracy, when SVMs were combined, was 88.4% (Moslem et al, 2011b).…”

Section: Term and Preterm Classificationmentioning

confidence: 98%

“…Using a feature set comprised of peak frequency, median frequency, root mean squares and sample entropy (extracted from the raw signals on Channel 3 in the 0.3-3 Hz frequency band), the algorithm was evaluated and the results show an overall accuracy of 70.82%. Support vector machines (SVM) have featured widely in research on preterm deliveries and are considered robust algorithms for classification tasks (Moslem et al, 2011a(Moslem et al, , 2011b(Moslem et al, , 2012. The primary focus has been to classify contractions as labour or non-labour events, using different locations on the abdomen.…”

Section: Term and Preterm Classificationmentioning

confidence: 99%

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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A Machine Learning Approach for an Early Prediction of Preterm Delivery

Despotovic

Zec

Mladenović

et al. 2018

2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY)

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Classification of multichannel uterine EMG signals by using a weighted majority voting decision fusion rule

Cited by 6 publications

References 14 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

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

A Machine Learning Approach for an Early Prediction of Preterm Delivery

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