1,720,959 research outputs found
Quantitative measures of autonomic activations during software development
No full textThis paper focuses on the analysis of autonomic nervous system responses of programmers during tasks of code comprehension and code writing. The signals analyzed are the heart rate variability and the respiratory signal, acquired using unobtrusive sensors connected to a polygraph. A bivariate time-variant autoregressive model was used to compute frequency domain features and their variations in time. A significant increase in heart rate and respiratory rate and a reduction in the total power of the heart rate variability were identified during code writing compared to other protocol tasks. This research is part of the second study of the BASE (Biofeedback Augmented Software Engineering) project
Cardiotocography in antenatal fetal monitoring of diabetes-complicated pregnancies
No full textReliable assessment of fetal health in pregnancies complicated by maternal diabetes is still challenging, but fundamental to reduce risks. Cardiotocography (CTG) is the most common tool in electronic fetal monitoring, but its utility in the management of diabetes-complicated pregnancies is still debated. Here we present some preliminary findings of a study aimed towards better defining the role of CTG in the management of pregnancies complicated by maternal diabetes by investigating the relation of CTG parameters with glycemic levels and pregnancy outcomes. A comprehensive set of standard and non-standard parameters was computed from the CTG traces collected at multiple gestational ages of 61 women with gestational diabetes, 25 with pregestational diabetes and 66 controls and related to pregnancy outcome and glucose levels. We identified an increase in the relative spectral power in the MF band and in the ternary Lempel Ziv Complexity in the diabetes-complicated pregnancies, as well as a week negative association between glucose level and signal variability. Associations with pregnancy outcomes are weak and call for further investigation
Discriminating Healthy and IUGR fetuses through Machine Learning models
No full textThe purpose of this study is to develop and understand whether Machine Learning models can classify Cardiotocographic (CTG) recordings of healthy fetuses or Intra Uterine Growth Restricted (IUGR) fetuses, highlighting how a large amount of data can have unexpected effects. We started from other findings in the literature to see what Machine Learning model remained consistent even with a large amount of data. The CTG records used in this study were collected at the Department of Obstetrics of the Federico II University Hospital in Naples, Italy, from 2013 to 2021. From this dataset, we chose 1548 IUGR fetuses and 1548 healthy fetuses to train our models. Each recording contained several parameters, ranging from features calculated on the entire CTG tracing, features calculated every 3 and 1 minute of recording and features related to the pregnant woman, such as age and week of gestation. We trained our machine-learning models on this dataset, checking the results obtained before and after adjusting the hyperparameters, noting that among the best models was Random Forest, which has already been present in other studies, and that the Multilayer Perceptron and the AdaBoost classifier were overall the best performing. This work can surely form a basis for future works in the fetal heart rate classification thus leading to real clinical applications
Fetal states identification in cardiotocographic tracings through discrete emissions multivariate hidden Markov models
No full textBackground and objectives: Computerized Cardiotocography (cCTG) allows to analyze the Fetal Heart Rate (FHR) objectively and thoroughly, providing valuable insights on fetal condition. A challenging but crucial task in this context is the automatic identification of fetal activity and quiet periods within the tracings. Different neural mechanisms are involved in the regulation of the fetal heart, depending on the behavioral states. Thereby, their correct identification has the potential to increase the interpretability and diagnostic capabilities of FHR quantitative analysis. Moreover, the most common pathologies in pregnancy have been associated with variations in the alternation between quiet and activity states. Methods: We address the problem of fetal states clustering by means of an unsupervised approach, resorting to the use of a multivariate Hidden Markov Models (HMM) with discrete emissions. A fixed length sliding window is shifted on the CTG traces and a small set of features is extracted at each slide. After an encoding procedure, these features become the emissions of a multivariate HMM in which quiet and activity are the hidden states. After an unsupervised training procedure, the model is used to automatically segment signals. Results: The achieved results indicate that our developed model exhibits a high degree of reliability in identifying quiet and activity states within FHR signals. A set of 35 CTG signals belonging to different pregnancies were independently annotated by an expert gynecologist and segmented using the proposed HMM. To avoid any bias, the physician was blinded to the results provided by the algorithm. The overall agreement between the HMM's predictions and the clinician's interpretations was 90%.Conclusions: The proposed method reliably identified fetal behavioral states, the alternance of which is an important factor in the fetal development. One key strength of our approach lies in the ease of interpreting the obtained results. By utilizing a small set of parameters that are already used in cCTG and possess clear intrinsic meanings, our method provides a high level of explainability. Another significant advantage of our approach is its fully unsupervised learning process. The states identified by our model using the Baum-Welch algorithm are associated with the "Active" and "Quiet" states only after the clustering process, removing the reliance on expert annotations. By autonomously identifying the clusters based solely on the intrinsic characteristics of the signal, our method achieves a more objective evaluation that overcomes the limitations of subjective interpretations. Indeed, we believe it could be integrated in cCTG systems to obtain a more complete signal analysis
Hidden Markov Models for the identification of fetal phases in CTG recordings
No full textCardiotocography (CTG) is the most widely used diagnostic examination to check the status of the fetus during pregnancy. The complex nature of CTG signals strongly affects the interpretation of the latter, causing a large intra and inter-observer variability in the analysis. A very challenging task in this context is the automatic identification of fetal activity and quiet periods within the tracings. The importance of distinguishing these periods resides in the fact that different neural mechanism are involved in the regulation of fetal heart, depending on quiet or activity fetal stages. This would also allow the evaluation of any differences present in diverse fetal stages between signals from healthy and pathological fetuses, increasing the diagnostic capability of CTG analysis. With the present work we address the problem of fetal state clustering in a totally unsupervised manner, resorting to the use of Hidden Markov Models. The obtained results have been brought to the attention of an experienced clinician, who expressed a 93% level of agreement between his interpretation and the fetal state clustering obtained with the model
Discriminating Healthy and IUGR fetuses through Machine Learning models
No full textThe purpose of this study is to develop and understand whether Machine Learning models can classify Cardiotocographic (CTG) recordings of healthy fetuses or Intra Uterine Growth Restricted (IUGR) fetuses, highlighting how a large amount of data can have unexpected effects. We started from other findings in the literature to see what Machine Learning model remained consistent even with a large amount of data. The CTG records used in this study were collected at the Department of Obstetrics of the Federico II University Hospital in Naples, Italy, from 2013 to 2021. From this dataset, we chose 1548 IUGR fetuses and 1548 healthy fetuses to train our models. Each recording contained several parameters, ranging from features calculated on the entire CTG tracing, features calculated every 3 and 1 minute of recording and features related to the pregnant woman, such as age and week of gestation. We trained our machine-learning models on this dataset, checking the results obtained before and after adjusting the hyperparameters, noting that among the best models was Random Forest, which has already been present in other studies, and that the Multilayer Perceptron and the AdaBoost classifier were overall the best performing. This work can surely form a basis for future works in the fetal heart rate classification thus leading to real clinical applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
