Application of noninvasive magnetomyography in labour imminency prediction for term and preterm pregnancies and ethnicity specific labour prediction

Abstract

This paper investigates the application of magnetomyography (MMG) signals from uterine contractions in pregnant patients towards the prediction of labour imminency within or above 48 h. The study utilised the MMG signals collected from a host of pregnant patients retrieved from a Physionet database, which also contained information regarding patients’ ethnicity and pregnancy. Utilising​ the information available in addition to the dataset, the study investigated the prospect of designing an ethnic specific labour imminency classifier to allow for an enhanced prediction, with an emphasis on Black and Caucasian ethnicities due to the nature of the data. Using an extended feature vector and a support vector machine (SVM) classifier, it was seen that the labour imminency was enhanced across the various classifier metrics considered in the ethnic specific classifier when compared with the generalised classifier. The results from the classification exercise, which considered the fusion of MMG signal information with the information on patients’ records, showed greater variability and a slightly lower classifier performance, thus suggesting that the MMG signals present a more reliable way of classifier training. Subsequent work in this area would now involve the application of optimisation algorithms to select an optimal number of electrodes that can be used for data acquisition, and thereby contributing towards the lowering of the cost associated with the implementation of the method using the MMG instrumentation.

Keywords

• Cybernetics
• Electromagnetism
• Decision support
• SVM
• Artificial intelligence
• Signal processing