A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

Abstract

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.

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data

“…An intercomparison of seven algorithms, including artificial neural networks (ANNs), long short-term memory networks (LSTMs), and random forests are reported elsewhere [11,12] on the CTG datasets, but did not accomplish the desired classification response in predicting the suspicious fetus state. This is attributed to the complexity of fetus dynamics and a considerable false-positive rate as indicated in previous studies [13,14]. Feedforward, multimodal and extreme learning networks (ELNs) are data-driven approaches.…”

A case study to investigate the impact of overcrowding indices in emergency departments

“…Several techniques have been adopted to improve the efficiency of the procedures in the ED [3,4]. In recent years, in particular, there has been an increasing use of data analysis and artificial intelligence to enhance biomedical data and signal analysis, for example as support for diagnosis [5,6], for the development of simulation models to support the characterization of flows [7][8][9] or for the optimization of processes through the support of appropriate performance indicators [10,11]. There are several studies that have provided for the implementation of these techniques directly in the ED, for the study of waiting times [12], length of stay [13,14] and drop-out rates [15,16].…”

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

“…Identifying the factors that cause prolonged LOS is essential to improve the patient’s condition and reduce healthcare costs [ 22 , 24 , 25 ]. Several studies report advanced processing of cardiac data for diagnostic purposes [ 26 , 27 , 28 , 29 , 30 , 31 ] or to support the monitoring process [ 32 , 33 ]. The aim of the present work is to determine the factors associated with prolonged hospitalization following endarterectomy, using the clinical and organizational data collected at the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital.…”