Epilepsy EEG signals classification based on sparse principal component logistic regression model.

Epilepsy is a chronic brain disease caused by excessive discharge of brain neurons. Long-term recurrent seizures bring a lot of trouble to patients and their families. Prediction of different stages of epilepsy is of great significance. We extract pearson correlation coefficients (PCC) between channels in different frequency bands as features of EEG signals for epilepsy stages prediction. However, the features are of large feature dimension and serious multi-collinearity. To eliminate these adverse influence, the combination of traditional dimension reduction method principal component analysis (PCA) and logistic regression method with regularization term is proposed to avoid over-fitting and achieve the feature sparsity. The experiments are conducted on the widely used CHB-MIT dataset using different regularization terms L1 and L2, respectively. The proposed method identifies various stages of epilepsy quickly and efficiently, and it presents the best average accuracy of 94.86%, average precision of 96.71%, average recall of 93.48%, average kappa value of 0.90 and average Matthews correlation coefficient (MCC) value of 0.90 for all patients.

Dual-Teacher Feature Distillation: A Transfer Learning Method for Insomniac PSG Staging.

Insomnia is the most common sleep disorder linked with adverse long-term medical and psychiatric outcomes. Automatic sleep staging plays a crucial role in aiding doctors to diagnose insomnia disorder. Only a few studies have been conducted to develop automatic sleep staging methods for insomniacs, and most of them have utilized transfer learning methods, which involve pre-training models on healthy individuals and then fine-tuning them on insomniacs. Unfortunately, significant differences in feature distribution between the two subject groups impede the transfer performance, highlighting the need to effectively integrate the features of healthy subjects and insomniacs. In this paper, we propose a dual-teacher cross-domain knowledge transfer method based on the feature-based knowledge distillation to improve the performance of sleep staging for insomniacs. Specifically, the insomnia teacher directly learns from insomniacs and feeds the corresponding domain-specific features into the student network, while the health domain teacher guide the student network to learn domain-generic features. During the training process, we adopt the OFD (Overhaul of Feature Distillation) method to build the health domain teacher. We conducted the experiments to validate the proposed method, using the Sleep-EDF database as the source domain and the CAP-Database as the target domain. The results demonstrate that our method surpasses advanced techniques, achieving an average sleep staging accuracy of 80.56% on the CAP-Database. Furthermore, our method exhibits promising performance on the private dataset.

Stability analysis and optimal control of COVID-19 with quarantine and media awareness.

In this paper, an improved COVID-19 model is given to investigate the influence of treatment and media awareness, and a non-linear saturated treatment function is introduced in the model to lay stress on the limited medical conditions. Equilibrium points and their stability are explored. Basic reproduction number is calculated, and the global stability of the equilibrium point is studied under the given conditions. An object function is introduced to explore the optimal control strategy concerning treatment and media awareness. The existence, characterization and uniqueness of optimal solution are studied. Several numerical simulations are given to verify the analysis results. Finally, discussion on treatment and media awareness is given for prevention and treatment of COVID-19.

An Automatic Method for Epileptic Seizure Detection Based on Deep Metric Learning.

Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of epilepsy which is a life-threatening neurological disorder. Many algorithms have been proposed for the automatic detection of epileptic seizures using traditional machine learning and deep learning. Although deep learning methods have achieved great success in many fields, their performance in EEG analysis and classification is still limited mainly due to the relatively small sizes of available datasets. In this paper, we propose an automatic method for the detection of epileptic seizures based on deep metric learning which is a novel strategy tackling the few-shot problem by mitigating the demand for massive data. First, two one-dimensional convolutional embedding modules are proposed as a deep feature extractor, for single-channel and multichannel EEG signals respectively. Then, a deep metric learning model is detailed along with a stage-wise training strategy. Experiments are conducted on the publicly-available Bonn University dataset which is a benchmark dataset, and the CHB-MIT dataset which is larger and more realistic. Impressive averaged accuracy of 98.60% and specificity of 100% are achieved on the most difficult classification of interictal (subset D) vs ictal (subset E) of the Bonn dataset. On the CHB-MIT dataset, an averaged accuracy of 86.68% and specificity of 93.71% are reached. With the proposed method, automatic and accurate detection of seizures can be performed in real time, and the heavy burden of neurologists can be effectively reduced.

A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion.

Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient's sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.

Novel methods to global Mittag-Leffler stability of delayed fractional-order quaternion-valued neural networks.

In this paper, a type of fractional-order quaternion-valued neural networks (FOQVNNs) with leakage and time-varying delays is established to simulate real-world situations, and the global Mittag-Leffler stability of the system is investigated by using the non-decomposition method. First, to avoid decomposing the system into two complex-valued systems or four real-valued systems, a new sign function for quaternion numbers is introduced based on the ones for real and complex numbers. And two novel lemmas for quaternion-valued sign function and Caputo fractional derivative are established in quaternion domain, which are used to investigate the stability of FOQVNNs. Second, a concise and flexible quaternion-valued state feedback controller is directly designed and a novel 1-norm Lyapunov function composed of the absolute values of real and imaginary parts is established. Then, based on the designed quaternion-valued state feedback controller and the proposed lemmas, some sufficient conditions are given to ensure the global Mittag-Leffler stability of the system. Finally, a numerical simulation is given to verify the theoretical results.

Pyrolysis Kinetic Study and Reaction Mechanism of Epoxy Glass Fiber Reinforced Plastic by Thermogravimetric Analyzer (TG) and TG-FTIR (Fourier-Transform Infrared) Techniques.

TG-FTIR combined technology was used to study the degradation process and gas phase products of epoxy glass fiber reinforced plastic (glass fiber reinforced plastic) under the atmospheres of high purity nitrogen. The pyrolysis characteristics of epoxy glass fiber reinforced plastic were measured under different heating rates (5, 10, 15, 20 °C min-1) from 25 to 1000 °C. The thermogravimetric analyzer (TG) and differential thermogravimetric analyzer (DTG) curves show that the initial temperature, terminal temperature, and temperature of maximum weight loss rate in the pyrolysis reaction phase all move towards high temperature, as the heating rate increases. Epoxy glass fiber reinforced plastic has two stages of thermal weightlessness. The temperature range of the first stage of weight loss is 290-460 °C. The second stage is 460-1000 °C. The above two weight loss stages are caused by pyrolysis of the epoxy resin matrix, and the glass fiber will not decompose. The dynamic parameters of glass fiber reinforced plastic were obtained through the Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO) and advanced Vyazovkin methods in model-free and the Coats-Redfern (CR) method in model fitting. FTIR spectrum result shows that the main components of the product gas are CO2, H2O, carbonyl components, and aromatic components during its pyrolysis.

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals.

Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)]. First, the structural features and connectivity features of the θ-, α-, and ß-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the θ-, α-, and ß-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes.

Finite-time synchronization of fractional-order gene regulatory networks with time delay.

As multi-gene networks transmit signals and products by synchronous cooperation, investigating the synchronization of gene regulatory networks may help us to explore the biological rhythm and internal mechanisms at molecular and cellular levels. We aim to induce a type of fractional-order gene regulatory networks to synchronize at finite-time point by designing feedback controls. Firstly, a unique equilibrium point of the network is proved by applying the principle of contraction mapping. Secondly, some sufficient conditions for finite-time synchronization of fractional-order gene regulatory networks with time delay are explored based on two kinds of different control techniques and fractional Lyapunov function approach, and the corresponding setting time is estimated. Finally, some numerical examples are given to demonstrate the effectiveness of the theoretical results.

A neural network model for visual selection and shifting.

In this paper, a two-layer network is built to simulate the mechanism of visual selection and shifting based on the mapping dynamic model for instantaneous frequency. Unlike the differential equation model using limit cycle to simulate neuron oscillation, we build an instantaneous frequency mapping dynamic model to describe the change of the neuron frequency to avoid the difficulty of generating limit cycle. The activity of the neuron is rebuilt based on the instantaneous frequency and in this work, we use the first layer of neurons to implement image segmentation and the second layer of neurons to act as visual selector. The frequency of the second neuron (central neuron) is always changing, while central neuron resonates with the neurons corresponding to an object, the object is selected, then with the central neuron frequency changing, the selected object loses attention, the process goes on.

Utilization of accident databases and fuzzy sets to estimate frequency of HazMat transport accidents.

Risk assessment and management of transportation of hazardous materials (HazMat) require the estimation of accident frequency. This paper presents a methodology to estimate hazardous materials transportation accident frequency by utilizing publicly available databases and expert knowledge. The estimation process addresses route-dependent and route-independent variables. Negative binomial regression is applied to an analysis of the Department of Public Safety (DPS) accident database to derive basic accident frequency as a function of route-dependent variables, while the effects of route-independent variables are modeled by fuzzy logic. The integrated methodology provides the basis for an overall transportation risk analysis, which can be used later to develop a decision support system.

Assessment of the effects of release variables on the consequences of LNG spillage onto water using FERC models.

Liquefied natural gas (LNG) release, spread, evaporation, and dispersion processes are illustrated using the Federal Energy Regulatory Commission models in this paper. The spillage consequences are dependent upon the tank conditions, release scenarios, and the environmental conditions. The effects of the contributing variables, including the tank configuration, breach hole size, ullage pressure, wind speed and stability class, and surface roughness, on the consequence of LNG spillage onto water are evaluated using the models. The sensitivities of the consequences to those variables are discussed.

Effects of fluid shear stress on apoptosis of cultured human umbilical vein endothelial cells induced by LPS.

The objective of our research was to reveal the effects of shear stress on the apoptosis of cultured human umbilical vein endothelial cells (HUVECs) induced by lipopolysaccharide (LPS). A parallel-plate flow chamber was used to control the strength and duration of shear stress (SS), and apoptosis was measured by immunocytochemistry and radio-immunoassay. Some important conclusions were drawn. In the stationary state, apoptosis of HUVECs could be induced by LPS (50 microg/ml). An SS of 15 dyn/cm(2) could inhibit the apoptosis induced by LPS. However, an SS of 4 dyn/cm(2) had less effect on the same process. At the same time, the experiment demonstrated that the increase in IL-6 secretion by LPS can be inhibited by two different levels of shear stress. Moreover, the inhibition effect was more obvious under high level stress than under low level. We also found that the effect of shear stress on IL-8 was less effective than on IL-6. This research provides data for understanding the mechanism of the contribution of hemodynamic forces to atherosclerosis.