Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification.

EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. To address these, we propose an efficient ensemble method called MLCSP-TSE-MLP, which aims to reduce the computational cost while achieving superior performance.

Di(2-ethylhexyl) phthalate mediates oxidative stress and activates p38MAPK/NF-kB to exacerbate diabetes-induced kidney injury in vitro and in vivo models.

Plasticizer di(2-ethylhexyl) phthalate (DEHP) is employed to make polyethylene polymers. Some studies in epidemiology and toxicology have shown that DEHP exposure over an extended period may be hazardous to the body, including nephrotoxicity, and aggravate kidney damage in the context of underlying disease. However, studies on the toxicity of DEHP in diabetes-induced kidney injury have been rarely reported.

DW-FBCSP: EEG emotion recognition algorithm based on scale distance weighted optimization.

Emotion calibration is measured by the valence and arousal scales and the ideal center is used to directly divide valence arousal into high scores and low scores. This division method has a big classification and labeling defect, and the influence of emotion stimulation material on the subjects cannot be accurately measured. To address this problem, this paper proposes an EEG emotion recognition algorithm (DW-FBCSP: Distance Weighted Filter Bank Common Spatial Pattern) based on scale distance weighted optimization to optimize the classification according to the distance of the scores from ideal center.

Analysis and Classification of Sleep Stages Based on Common Frequency Pattern From a Single-Channel EEG Signal.

One crucial key of developing an automatic sleep stage scoring method is to extract discriminative features. In this paper, we present a novel technique, termed common frequency pattern (CFP), to extract the variance features from a single-channel electroencephalogram (EEG) signal for sleep stage classification. The learning task is formulated by finding significant frequency patterns that maximize variance for one class and that at the same time, minimize variance for the other class.

Validation of Pulse Transit Time Based Blood Pressure Estimation on Atrial Fibrillation Patients.

Pulse transit time (PTT) based continuous cuff-less blood pressure (BP) monitoring has attracted wide interests owing to its potential in improving the control and early prevention for cardiovascular diseases. However, it is still impractical in large-scale clinical application due to the concern of BP measurement accuracy. Since such approach strongly relies on the PTT-BP model under certain theoretical assumptions, the accuracy would be affected by the vessel properties alterations induced by cardiovascular disorders.

Tensor Discriminant Analysis for MI-EEG Signal Classification Using Convolutional Neural Network.

Motor Imagery (MI) is a typical paradigm for Brain-Computer Interface (BCI) system. In this paper, we propose a new framework by introducing a tensor-based feature representation of the data and also utilizing a convolutional neural network (CNN) architecture for performing classification of MI-EEG signal. The tensor-based representation that includes the structural information in multi-channel time-varying EEG spectrum is generated from tensor discriminant analysis (TDA), and CNN is designed and optimized accordingly for this representation.

Cuffless blood-pressure estimation method using a heart-rate variability-derived parameter.

Objective: Cuffless blood-pressure (BP) estimation has attracted widespread interest owing to its potential for long-term, non-invasive BP monitoring. But it is still impractical in clinical settings, mainly owing to ongoing challenges with respect to accuracy in hypertensive patients. To better estimate the BP, the current study proposes a new cuffless estimation method that includes a sympathetic tone, which has been reported with a varied pattern in hypertensive patients.