Giovanni Magenes

Maria G. Signorini

Frontiers in Bioengineering and Biotechnology

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Editorial: Biomedical engineeringtechnologies and methods inantenatal medicineGiovanni Magenes1,2*† and Maria G. Signorini3†1Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, 2Centrefor Health Technologies, University of Pavia, Pavia, Lombardy, Italy, 3Department of Electronics,Information and Bioengineering, Polytechnic University of Milan, Milan, Lombardy, ItalyKEYWORDSfetal monitoring, fetal heart rate analysis, cardiotocography (CTG), signal processing,machine learning methodsEditorial on the Research TopicBiomedical engineering technologies and methods in antenatal medicineAlthough pregnancy by itself should not be considered a pathological condition or a life-threatening event, the antepartum period is usually a very critical time window for bothmothers and their fetuses. Pregnancy and consequently the complex interactions takingplace at the level of the mother-fetus system are characterized by a peculiar trait: fast andcontinuous evolution in time. As a matter of fact the fetus passes from 2 cells to 3 kilos inabout 9 months and the mother’s body must rapidly adapt. Therefore, careful monitoring ofboth subjects before delivery and during labor has been reported as an effective tool forpreventing a set of possible unfavorable outcomes (Grivell et al., 2012).In principles, a continuous tracking of pregnancy evolution in time would be the bestway to proper address maternal and fetal wellbeing, however, even in the most developedcountries, healthcare systems foresee an average of 3 monitoring sessions during pregnancy.Hence, these examinations are basically a series of snapshots of mother/fetus wellbeing, farapart one each other.In this context, it is desirable to design medical devices and software tools capable ofproviding an effective check-up of pregnant women in several kinds of environment anddaily life situations. The aim is to establish reliable health assessments in both acute(bleeding, severe nausea, abdominal pain), or non-acute situations of distress(preeclampsia, gestational diabetes, sporadic contraction, decline in fetus’ activity), forboth mothers and fetuses and, eventually, to prevent them.This Research Topic of Frontiers in Bioengineering and Biotechnology aimed at presentingnew solutions for antenatal medicine from both the technological and methodological sides.Four papers were accepted and published, covering several aspects of pre-natal monitoring.Three of them deal with the most used fetal monitoring technology, theCardiotocography (CTG), and try to provide new methodological tools for betteridentifying fetal critical conditions both in the antenatal period and during labour.In particular, Ribeiro et al. present an exploratory work, which aims to developintrapartum predictive models of perinatal asphyxia based on clinical parameters andfetal heart rate (fHR) indices. Perinatal asphyxia is one of the most frequent causes ofneonatal mortality. Cardiotocographic signals (CTGs) in association with various clinicalparameters were employed for the development of Binary logistic regression (BLR) andOPEN ACCESSEDITED BYShi Yuan,Children’s Hospital of Chongqing MedicalUniversity, ChinaREVIEWED BYMicaela Morettini,Marche Polytechnic University, Italy*CORRESPONDENCEGiovanni Magenes,giovanni.magenes@unipv.it†These authors have contributed equallyto this workRECEIVED 12 June 2023ACCEPTED 19 July 2023PUBLISHED 01 August 2023CITATIONMagenes G and Signorini MG (2023),Editorial: Biomedical engineeringtechnologies and methods inantenatal medicine.Front. Bioeng. Biotechnol. 11:1239031.doi: 10.3389/fbioe.2023.1239031COPYRIGHT© 2023 Magenes and Signorini. This is anopen-access article distributed under theterms of the Creative CommonsAttribution License (CC BY). The use,distribution or reproduction in otherforums is permitted, provided the originalauthor(s) and the copyright owner(s) arecredited and that the original publicationin this journal is cited, in accordance withaccepted academic practice. No use,distribution or reproduction is permittedwhich does not comply with these terms.Frontiers in Bioengineering and Biotechnology frontiersin.org01TYPE EditorialPUBLISHED 01 August 2023DOI 10.3389/fbioe.2023.1239031Naive-Bayes (NB) models. In the analyzed data (517 cases, with15 pathological cases), he BLR model shows the best performance inpredicting perinatal asphyxia.Feng et al. propose tomodel intrapartum CTG from a dynamicalsystem perspective. Empirical dynamic modeling with Gaussianprocesses is used, which is a Bayesian nonparametric approachfor function estimation. The capacity of Gaussian processes allowsfor revealing causal relationships between time series. Results on realCTG recordings show that fetal heart rate (FHR) and uterine activity(UA) signals are causally related. The authors emphasize that thiscausal relationship and the estimated attractor manifolds can beexploited for applications in the computerized analysis of CTGrecords.The contribution by Spairani et al. proposes a hybrid MachineLearning approach based on a neural architecture that receivesheterogeneous data in input (quantitative parameters and imagesbuilt from fHR signal) for classifying healthy and pathologicalfetuses. Improvements in the classification performance are achievedby setting a supervised network owing two connected branches,consisting respectively of a Multi-Layer Perceptron (MLP) and aConvolutional Neural Network (CNN). The classification systemwas trained with a considerable number of fHR tracings,7,000 healthy and 7,000 pathological, recorded during prenatalambulatory non stress tests. The classification network reaches anoverall promising accuracy of 80.1%.The fourth paper considers multi-channel fetal monitoring bymeans of abdominal ECG. This technology, with respect to classical USCTG fetal monitoring, can be used for long term monitoring duringeveryday life of the pregnant woman through wearable systems.However, fECG should be extracted from signals containing alsomaternal ECG, and this process implies both to select the best leadsfor fECG and to apply robust and reliable algorithms for its separationfrom maternal ECG. Baldazzi et al. propose a machine learningapproach for the Signal Quality Assesment that drives channelselection to feed fully featured fECG extraction algorithms. Thisapproach can enhance fetal QRS detection performance byidentifying the most-informative channels in high-densityrecordings, or the best electrode positioning from repeatedmeasurements with a low number of channels.The research and development work presented in the papers ofthis special section demonstrates that advanced signal processingand wearable technologies are making big strides toward theassessment of a quantitative approach in the field of antenatalmedicine. Despite a qualitative evaluation of fetal monitoring isstill the most used approach in the clinical practice, we have recentlywitnessed growing interest and significant developments in methodsfocused on extracting quantitative features from fECG and/or CTGsignals. We trust that the readers of Frontiers in Bioengineering andBiotechnology will find the material presented in this ResearchTopic to be stimulating and illustrating a good picture of thestate of the art in the field.Author contributionsAll authors listed havemade a substantial, direct, and intellectualcontribution to the work and approved it for publication.Conflict of interestThe authors declare that the research was conducted in theabsence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.Publisher’s noteAll claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, orclaim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.ReferenceGrivell, R. M., Alfirevic, Z., Gyte, G. M., and Devane, D. (2012). Antenatalcardiotocography for fetal assessment. Cochrane Database Syst. Rev. 20 (1),CD007863. doi:10.1002/14651858.cd007863.pub4Frontiers in Bioengineering and Biotechnology frontiersin.org02Magenes and Signorini 10.3389/fbioe.2023.1239031

Editorial: Biomedical engineering technologies and methods in antenatal medicine

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Editorial: Biomedical engineering technologies and methods in antenatal medicine

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