Key result
Approximate entropy leads classification and Daubechies wavelets dominate signal processing for fetal heart rate analysis.
Why the study?
Many mathematical analyses evaluate fetal heart rate variability, but a review was needed to summarize non-linear methods and investigate how they relate to specific research objectives.
Can non-linear methods of fetal heart rate analysis assess prenatal conditions such as hypoxia and fetal distress?
Systematic Review (n=270)
Can non-linear methods of fetal heart rate analysis assess prenatal conditions such as hypoxia and fetal distress?
Non-linear indices of fetal heart rate variability show promise for assessing prenatal conditions but require further standardization before clinical application.
Approximate entropy and Daubechies wavelets dominate fetal heart rate studies; extends non-linear method research but leaves clinical standardization open.
The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.
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Ribeiro et al. (2021) conducted a systematic review in Fetal heart rate analysis (n=270). Non-linear methods (entropy, compression, fractal analysis, wavelets) was evaluated on Most used non-linear methods and primary research objectives. In a systematic review of 270 articles, approximate entropy was the most used method in classification papers, while Daubechies wavelets were most used in signal processing for fetal heart rate analysis.
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