20 research outputs found
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals
The availability of standardized guidelines regarding the use of electronic fetal monitoring
(EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate
(FHR) surveillance methodology, which still presents inter- and intra-observer variability as well
as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical
relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing
autonomous nervous system development, many different approaches for computerized processing
and analysis of FHR patterns have been proposed in the literature. The objective of this review is to
describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their
main achievements and discussing the value they brought to the scientific and clinical community.
The review explores the following two main approaches to the processing and analysis of FHR
signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less
conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities
offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed
with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of
accelerations in FHR signals is also examined in a case study conducted by the authors
Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals
In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate
(FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing.
Despite the availability of several approaches to analyze the variability of FHR signals (namely
the FHRV), there are still shadows hindering a comprehensive understanding of how linear and
nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose
a straightforward processing and modeling route for a deeper understanding of the relationships
between the characteristics of the FHR signal. A multiparametric modeling and investigation of the
factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out
by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural
networks. The obtained results show that linear features are more influential compared to nonlinear
ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation
of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken
carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable
and reliable information to clinicians and researchers
Fetal Heart Rate Fragmentation
This article was supported by National Funds through FCT– Fundação para a Ciência e a Tecnologia, I.P., within CINTESIS, R&D Unit (reference UIDB/4255/2020)info:eu-repo/semantics/publishedVersio
Non Invasive Foetal Monitoring with a Combined ECG - PCG System
Although modern ultrasound provides remarkable images and biophysical measures, the technology is expensive and the observations are only available over a short time. Longer term monitoring is achieved in a clinical setting using ultrasonic Doppler cardiotocography (CTG) but this has a number of limitations. Some pathologies and some anomalies of cardiac functioning are not detectable with CTG. Moreover, although frequent and/or long-term foetal heart rate (FHR) monitoring is recommended, mainly in high risk pregnancies, there is a lack of established evidence for safe ultrasound irradiation exposure to the foetus for extended periods (Ang et al., 2006). Finally, high quality ultrasound devices are too expensive and not approved for home care use. In fact, there is a remarkable mismatch between ability to examine a foetus in a clinical setting, and the almost complete absence of technology that permits longer term monitoring of a foetus at home. Therefore, in the last years, many efforts (Hany et al., 1989; Jimenez et al., 1999; Kovacs et al., 2000; Mittra et al., 2008; Moghavvemi et al., 2003; Nagal, 1986; Ruffo et al., 2010; Talbert et al., 1986; Varady et al., 2003) have been attempted by the scientific community to find a suitable alternative