A machine learning system for automatic detection of preterm activity using artificial neural networks and uterine electromyography data. (2015)

• Record Type: Journal Article
• Authors: Fergus, Paul; Hussain, Abir; Al-Jumeily, D.; Hamdan, Hani
• Is Part Of: International journal of adaptive and innovative systems. Volume 2:Number 2(2015)
• Page Start: 161
• Page End: 179
• Publication Date: 2015
• Subjects: term delivery -- preterm delivery -- machine learning -- classification -- electrohysterography -- EHG -- artificial neural networks -- ANNs -- uterine EMG data -- electromyography -- preterm births -- premature delivery -- premature babies; Adaptive computing systems -- Periodicals; Computer systems -- Technological innovations -- Periodicals; Systems software -- Periodicals; 004
• Languages: English
• ISSNs: 1740-2107

Abstract

Preterm births are babies born before 37 weeks of gestation. The premature delivery of babies is a major global health issue with those affected at greater risk of developing short and long-term complications. Therefore, a better understanding of why preterm births occur is needed. 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. 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.