ICT4MOMs: An ICT Integrated Approach to Monitor and Manage Pregnancy Development

ICT4MOMs project aims at implementing a novel remote ICT service towards the monitoring and prediction of maternal and fetal conditions throughout pregnancy. The envisioned application is based on the integration of wearable sensors and devices connected by an ad-hoc smartphone app in communication with an ob-gyn clinical center. Advanced signal and image processing software tools will be developed for extracting information from the recorded signals, namely: fetal heart rate, uterine contractions, continuous glucose sensors and portable US probe. Once validated by the clinical partners, the collected dataset will be used for a multivariate analysis based on soft computing classifiers and machine learning techniques. Based on the growing literature providing evidence on the fact that mother-fetus system should be considered as a whole, in this proposal pregnancy is conceptualized as a continuously evolving system which needs to be investigated by means of time-varying approaches. The crucial expected outcome is the integration of the established clinical knowledge with the results of computational analysis. Such multilevel integration is expected to provide reliable and translatable clinical guidelines towards a novel pregnancy management encompassing a more inclusive monitoring framework designed on a patient-specific level. The project was recently funded by the Italian Government—Progetti di Interesse Nazionale (PRIN) under the grant number 2017RR5EW3 for the duration of three years (2019–2021).

[1]  Marta Campanile,et al.  Four years' experience with antepartum cardiotocography using telemedicine , 2006, Journal of telemedicine and telecare.

[2]  Riccardo Bellazzi,et al.  Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[3]  H. Geijn 2 Developments in CTG analysis , 1996 .

[4]  Luigi Raffo,et al.  NInFEA: an embedded framework for the real-time evaluation of fetal ECG extraction algorithms , 2013, Biomedizinische Technik. Biomedical engineering.

[5]  James R. Warren,et al.  The role of home-based information and communications technology interventions in chronic disease management: a systematic literature review , 2009, Health Informatics J..

[6]  J. M. Swartjes,et al.  Computer analysis of antepartum fetal heart rate: 1. Baseline determination. , 1990, International journal of bio-medical computing.

[7]  J. M. Swartjes,et al.  Computer analysis of antepartum fetal heart rate: 2. Detection of accelerations and decelerations. , 1990, International journal of bio-medical computing.

[8]  Giuseppe Andreoni,et al.  Telefetalcare: A first prototype of a wearable fetal electrocardiograph , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.