Nonlinear analysis of heart rhythm in preeclampsia: a route for translational clinical applications in neuroinflammation

Preeclampsia is a pregnancy-specific condition which gets detected through hypertension and excessive protein excretion in urine. While preeclampsia used to be regarded as a self-limiting maternal condition which resolved with the delivery of the placenta, it is nowadays considered a complex and multifactorial disease that affects the offspring. Unfortunately, the etiology and pathophysiology of this multifaceted disorder remain elusive. Recent findings have confirmed that an altered maternal autonomic function may play a vital role in developing preeclampsia in conjunction with an imbalanced maternal immune system. Additionally, further evidence supports the crucial role of an exacerbated immune response driven by a non-infectious trigger during preeclampsia. Therefore, as a sterile inflammation, the elucidation of the neuroinflammatory mechanisms of preeclampsia warrants obtaining relevant knowledge suitable for translational clinical applications.Heart rate variability (HRV) is an affordable and non-invasive method for indirectly assessing the autonomic nervous system and the cholinergic anti-inflammatory pathway (CAP). Notably, the nonlinear analysis of HRV offers novel indexes to explore the neuroimmune interactions in diverse preclinical and clinical settings of inflammation. Given that the dynamics of HRV is nonlinear in health, we hypothesized that a neuroinflammatory condition in preeclampsia might be associated with changes in nonlinear features of maternal and fetal HRV. Thus, the present review aims to present evidence of the potential changes in maternal-fetal HRV associated with neuroinflammatory modifications in preeclamptic women. We considered that there is still a need for assessing the nonlinear features of maternal and fetal HRV as complementary biomarkers of inflammation in this population in future studies, being a potential route for translational clinical applications.

Flexible Bayesian Nonlinear Model Configuration

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modi ed mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

The Soundtrack of the Sinister Trailer

In this article, I analyse the soundtrack of the green band trailer for Sinister (Scott Derrickson, 2012), combining quantitative methods to analyse the soundtrack with formal analysis. I show that, even though Sinister is a narrative about a demon who lives in images, the horror in the soundtrack of this trailer is articulated through the sound design. I describe the structure of the soundtrack and analyse the distribution and organisation of dialogue, the use of different types of sound effects to create a connection between the viewer and the characters onscreen, as well as the use of specific localised sound events to organise attention and to frighten the viewer. I identify two features not previously discussed in relation to quantitative analysis of film soundtracks: an affective event based on reactions to a stimulus and the presence of nonlinear features in the sound envelopes of localised affective events. The sound design of this trailer is consistent with the principles of contemporary sound design in horror cinema, but also demonstrates some variation in its use of sound as a paratext to its parent film.

THE NEW MODIFICATION OF THE APPROXIMATING FUNCTIONS METHOD FOR CLOUD COMPUTING

The approach for building cloud-ready fault-tolerant calculations by approximating functions method, which is an analytical-numerical part of Volterra integral equation method for solving 1D+T nonlinear electromagnetic problems, is presented. The solving process of the original algorithm of the method is modified: it is broken down into the sequential chain of stages with a fixed number of sequential or parallel steps, each of which is built in a fault-tolerant manner and saves execution results in fault-tolerant storage for high availability. This economizes RAM and other computer resources and does not damage the calculated results in the case of a failure, and allows stopping and starting the calculations easily after manual or accidental shutdown. Also, the proposed algorithm has self-healing and data deduplication for cases of corrupted saved results. The presented approach is universal and does not depend on the type of medium or the initial signal. Also, it does not violate the natural description of non-stationary and nonlinear features, the unified definition of the inner and outer problems, as well as the inclusion of the initial and boundary conditions in the same equation as the original approximating functions method. The developed approach stress-tested on the known problems, stability checked and errors compared.

Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis

Background Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. Methods In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). Results According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. Conclusion The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

The New Modification of the Approximating Functions Method for Cloud Computing

The approach for building cloud-ready fault-tolerant calculations by approximating functions method, which is an analytical-numerical part of Volterra integral equation method for solving 1D+T nonlinear electromagnetic problems, is presented. The solving process of the original algorithm of the method is modified: it is broken down into the sequential chain of stages with a fixed number of sequential or parallel steps, each of which is built in a fault-tolerant manner and saves execution results in fault-tolerant storage for high availability. This economizes RAM and other computer resources and does not damage the calculated results in the case of a failure, and allows stopping and starting the calculations easily after manual or accidental shutdown. Also, the proposed algorithm has self-healing and data deduplication for cases of corrupted saved results. The presented approach is universal and does not depend on the type of medium or the initial signal. Also, it does not violate the natural description of non-stationary and nonlinear features, the unified definition of the inner and outer problems, as well as the inclusion of the initial and boundary conditions in the same equation as the original approximating functions method. The developed approach stress-tested on the known problems, stability checked and errors compared.

Correlation between Spatial Fractal and Temporal Chaos During the Sliding Wear Process of AISI 5120 Steel

The purpose of this paper is to establish the relationship between surface morphology and friction coefficient in the wear process. Different wear stage tests of AISI 52100 ring sliding against AISI 5120 disc were designed and conducted on a rotating setup. The fractal and chaos theories were employed to study the nonlinear features of surface structure and friction signal from spatial and temporal scales. The results showed that 3D surface morphology has fractal nature. The fractal dimension Ds first increased and then stabilized at a maximum and finally decreases dramatically. The multifractal spectrum width Δα presented an contrary evolution trend. The friction coefficient signal has chaotic nature. The standard deviation of distance matrix STD obeyed the evolution rule of a bathtub curve. The correlation value between Ds and STD was − 0.7727, and the correlation value between Δα and STD was 0.7130. The strong correlation between spatial and temporal scales is beneficial to on-line recognition and prediction of wear states in real time.