A framework for intelligent analysis of digital cardiotocographic signals from IoMT-based foetal monitoring

Improving the accuracy and consistency of interpretation results for foetal monitoring has been an active research direction in both obstetrics and gynaecology. In this paper, we have developed a novel framework for intelligent analysis and automatic interpretation of digital cardiotocographic signals recorded from the Internet of Medical Things (IoMT)-based foetal monitors. The framework incorporates methods and systems that evaluate the foetal conditions in the cavity of the uterus. The methods can accurately identify various critical features of cardiotocographic signals, thus making the interpretation results more accurate. The systems are used both in hospitals and at home, and not only analyse any segment of data in a record, but also implement a number of popular automatic scoring functions, including the Kreb's, Fischer, and modified Fischer and the ACOG three-tier classification. According to clinical tests in hospitals, our framework has comparable accuracy to obstetricians' interpretations. It thus provides a supplement to traditional analysis that could help obstetricians function more effectively. (C) 2019 Elsevier B.V. All rights reserved.