Complexity analysis of the fetal heart rate variability: early identification of severe intrauterine growth-restricted fetuses

The main goal of this work is to suggest new indices for a correct identification of the intrauterine growth-restricted (IUGR) fetuses on the basis of fetal heart rate (FHR) variability analysis performed in the antepartum period. To this purpose, we analyzed 59 FHR time series recorded in early periods of gestation through a Hewlett Packard 1351A cardiotocograph. Advanced analysis techniques were adopted including the computation of the Lempel Ziv complexity (LZC) index and the multiscale entropy (MSE), that is, the entropy estimation with a multiscale approach. A multiparametric classifier based on k-mean cluster analysis was also performed to separate pathological and normal fetuses. The results show that the proposed LZC and the MSE could be useful to identify the actual IUGRs and to separate them from the physiological fetuses, providing good values of sensitivity and accuracy (Se = 77.8%, Ac = 82.4%)

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

<p>Abstract</p> <p>Background</p> <p>In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several <it>concerns </it>about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these <it>concerns </it>and identify means of enhancing the current models for higher operational use.</p> <p>Methods</p> <p>We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.</p> <p>Results</p> <p>While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.</p> <p>Conclusions</p> <p>To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.</p

ps4 79 long term follow up of 320 chilren born to mothers with systemic autoimmune diseases a multicentre survey from 24 rheumatology centres in italy

Background Rheumatic Diseases (RD) frequently affect women during reproductive age, therefore counselling on family planning is crucial for their quality of life. Children's outcome is a major topic, but no large studies are available. This study aimed at assessing the long-term health conditions of children born to women with RD. Methods 24 Italian Rheumatology Centres distributed the questionnaire (65 multiple-choice and 12 open-answer questions) to consecutive patients (aged 18–55) during September 2015. Data were analysed dividing children upon maternal diagnosis: Chronic Arthritides (CA) and Connective Tissue Diseases (CTD). Results Data were collected for 320 children born to 184 mothers (63 CA and 121 CTD). At the time of interview, children had a mean age of 17.1±9.6 years. Pre-term delivery ( The occurrence of an autoimmune/inflammatory disease (AIID) and/or neurodevelopmental disorders (ND)/learning disabilities (LD) is reported in table 1. Twelve children (3.7%) were diagnosed with an AIID, mostly coeliac disease (8/12, 67%). Eleven children (3.4%) were diagnosed as having a ND and/or LD by a Paediatric Neuropsychiatrist. Data of in utero exposure to maternal autoantibodies and/or anti-rheumatic drugs were retrieved for 280 children (87.5%) and a comparison was performed between affected (n=11) and not-affected children (n=258). No association was found with ND/LD and in utero exposure to autoantibodies (ANA, anti-Ro, anti-dsDNA, aPL) or drugs (HCQ,AZA or steroids), neither with sex, preterm birth, birth weight or maternal diagnosis. Conclusions The long-term follow-up of children born to mothers with RD did not raise particular concerns in terms of relevant health problems. In particular, each AIID did not display a significantly increased frequency as compared to the literature. Children with ND/LD had a tendency to cluster in the group of mothers with CTD, especially after maternal diagnosis, with a higher frequency as compared to GPP (7.9% vs 3%). Our data suggest that the development of ND/LD in children of patients with RD cannot be linked exclusively to maternal disease. The results of this study can be reassuring for patients with RD about problems in the offspring possibly related to their disease

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Detection of Fetal Distress though a Support Vector Machine Based on Fetal Heart Rate Parameters

This work aimed at realizing an automatic system for diagnosing fetal sufferance through advanced classification methods applied to reliable indexes extracted from fetal heart rate (FHR) recordings. We selected a set of FHR recordings from a database of 909 exams, which were supplied with the diagnosis at the delivery. The analysis was based on both classical parameters taken from the obstetrical clinical literature and some new indexes already used for HR variability in adults, like the power spectral density (PSD) and the approximate entropy (ApEn). This parameter set was then used as input of a learning machine based on the support vector machine (SVM) algorithm. We obtained a dichotomic classifier, performing the detection of suffering IUGR fetuses from healthy ones. A high percentage of correct classifications, above 84%, was reached by filtering the training set with only 65 of the starting 909 available records. 1