5,830 research outputs found
A Machine Learning Approach to Monitor the Emergence of Late Intrauterine Growth Restriction
Late intrauterine growth restriction (IUGR) is a fetal pathological condition characterized by chronic hypoxia secondary to placental insufficiency, resulting in an abnormal rate of fetal growth. This pathology has been associated with increased fetal and neonatal morbidity and mortality. In standard clinical practice, late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This study presents a radial basis function support vector machine (RBF-SVM) classification based on quantitative features extracted from fetal heart rate (FHR) signals acquired using routine cardiotocography (CTG) in a population of 160 healthy and 102 late IUGR fetuses. First, the individual performance of each time, frequency, and nonlinear feature was tested. To improve the unsatisfactory results of univariate analysis we firstly adopted a Recursive Feature Elimination approach to select the best subset of FHR-based parameters contributing to the discrimination of healthy vs. late IUGR fetuses. A fine tuning of the RBF-SVM model parameters resulted in a satisfactory classification performance in the training set (accuracy 0.93, sensitivity 0.93, specificity 0.84). Comparable results were obtained when applying the model on a totally independent testing set. This investigation supports the use of a multivariate approach for the in utero identification of late IUGR condition based on quantitative FHR features encompassing different domains. The proposed model allows describing the relationships among features beyond the traditional linear approaches, thus improving the classification performance. This framework has the potential to be proposed as a screening tool for the identification of late IUGR fetuses
Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76
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 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
Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals
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
Pregnancy monitoring
International audienc
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