Abstract
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.
Highlights
As widely showed in the literature concerning heart rate variability [1,2,3,4], computerized signal processing techniques proved to be an effective way to detect different types of cardiovascular disorders
In this study we showed that Artificial Neural Networks (ANN) proved to be a promising tool to get insight into the relationships between the certain characteristics of the fetal heart rate (FHR) signals and the linear and nonlinear Fetal Heart (FHRV)
The obtained results suggest that nonlinear dynamics can have an impact on the control of FHR accelerations in healthy fetuses, and this is far more evident when the dynamics are compared at largely different ranges of FHR mean and pregnancy periods
Summary
As widely showed in the literature concerning heart rate variability [1,2,3,4], computerized signal processing techniques proved to be an effective way to detect different types of cardiovascular disorders. The efforts towards the establishment of a standard technique for the processing and analysis of FHR signals brought the development of different software solutions, methodological approaches, and indicators that could assist the clinical examination of CTG recordings, with particular regard to the FHR signals [18,19,20,21,22,23,24]. As happened in the analysis of adult and newborn heart rate signals [25,26,27,28], most of the newer computerized tools for FHR processing and analysis are based on Artificial Intelligence (AI) algorithms aimed at extracting novel features from the FHR signals, and achieve a more accurate classification of the traces according to the fetal health status [29,30,31,32]. Machine learning algorithms and, in particular, Artificial Neural Networks (ANN) showed promising results in terms of predictability and classification capabilities [31,32,33,34,35,36]
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