A study on preterm birth predictions using physiological signals, medical health record information and low‐dimensional embedding methods. Issue 3 (6th September 2021)

• Record Type: Journal Article
• Authors: Nsugbe, Ejay; Samuel, Oluwarotimi William; Sanusi, Ibrahim; Asogbon, Mojisola Grace; Li, Guanglin
• Is Part Of: IET cyber-systems and robotics. Volume 3:Issue 3(2021)
• Page Start: 228
• Page End: 244
• Publication Date: 2021-09-06
• Subjects: cybernetics -- decision‐making -- decision‐tree classifier -- machine intelligence -- machine learning -- sensor fusion; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/csy2.12031
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract: Preterm births have been seen to have psychological and financial implications; current surveys suggest that amongst the various methods of preterm prediction, there is yet to exist a reliable and standard means of predicting preterm births. This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester, alongside information from the patients' medical health record regarding the pregnancy, towards preterm prediction and an associated delivery imminency timeline. In addition to this, the impact of both linear and non‐linear dimensional embedding methods towards the preterm prediction is explored. The classification exercises were carried out using a support vector machine and decision tree, both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.