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 unc...
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doaj-52e568fb45da4909b226fadc003a8fc62021-09-26T01:22:54ZengMDPI AGSensors1424-82202021-09-01216136613610.3390/s21186136A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate SignalsAlfonso Maria Ponsiglione0Carlo Cosentino1Giuseppe Cesarelli2Francesco Amato3Maria Romano4Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Experimental and Clinical Medicine ‘Gaetano Salvatore’, University Magna Graecia of Catanzaro, Viale Tommaso Campanella 185, 88100 Catanzaro, ItalyDepartment of Chemical, Materials and Production Engineering (DICMaPI), University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyThe 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.https://www.mdpi.com/1424-8220/21/18/6136fetal heart ratefetal heart rate variabilitybiomedical signal processing and analysislinear FHRV indicesnonlinear FHRV indicesartificial neural networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alfonso Maria Ponsiglione Carlo Cosentino Giuseppe Cesarelli Francesco Amato Maria Romano |
spellingShingle |
Alfonso Maria Ponsiglione Carlo Cosentino Giuseppe Cesarelli Francesco Amato Maria Romano A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals Sensors fetal heart rate fetal heart rate variability biomedical signal processing and analysis linear FHRV indices nonlinear FHRV indices artificial neural networks |
author_facet |
Alfonso Maria Ponsiglione Carlo Cosentino Giuseppe Cesarelli Francesco Amato Maria Romano |
author_sort |
Alfonso Maria Ponsiglione |
title |
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals |
title_short |
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals |
title_full |
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals |
title_fullStr |
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals |
title_full_unstemmed |
A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals |
title_sort |
comprehensive review of techniques for processing and analyzing fetal heart rate signals |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-09-01 |
description |
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. |
topic |
fetal heart rate fetal heart rate variability biomedical signal processing and analysis linear FHRV indices nonlinear FHRV indices artificial neural networks |
url |
https://www.mdpi.com/1424-8220/21/18/6136 |
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