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
