Clinical electromagnetic brain scanner.

Stroke is a leading cause of death and disability worldwide, and early diagnosis and prompt medical intervention are thus crucial. Frequent monitoring of stroke patients is also essential to assess treatment efficacy and detect complications earlier. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they cannot be easily used onsite, nor for frequent monitoring purposes.

Case Report: Preliminary Images From an Electromagnetic Portable Brain Scanner for Diagnosis and Monitoring of Acute Stroke.

Electromagnetic imaging is an emerging technology which promises to provide a mobile, and rapid neuroimaging modality for pre-hospital and bedside evaluation of stroke patients based on the dielectric properties of the tissue. It is now possible due to technological advancements in materials, antennae design and manufacture, rapid portable computing power and network analyses and development of processing algorithms for image reconstruction. The purpose of this report is to introduce images from a novel, portable electromagnetic scanner being trialed for bedside and mobile imaging of ischaemic and haemorrhagic stroke.

Impact of clean air action on the PM pollution in Beijing, China: Insights gained from two heating seasons measurements.

Comprehensive observations have been carried out in Beijing to investigate the impact of the Clean Air Action implemented in 2013 on changes in aerosol chemistry characteristics in heating seasons of 2016-2017 and 2017-2018. Results showed that PM, SO, NO, NH, O and CO concentrations decreased by 40.9%, 46.

Prevalence of Hepatitis B Virus Infection in Shenzhen, China, 2015-2018.

China has nearly 10% of the general HBV carrier population in the world; this infection is the most common cause of chronic liver disease. Understanding HBV epidemiology is essential for future infection control, evaluation, and treatment. This study determined the prevalence of HBV infection in Shenzhen by serological testing and analysis in 282,166 HBV screening cases for the following: HBcAb, indicative of previous HBV infection; HBsAg, indicative of chronic (current) infection; HBsAb, indicative of immunity from vaccination; and 34,368 HBV etiological screening cases for HBV-DNA, indicative of virus carriage, in which 1,204 cases were genotyped and mutation analyzed for drug-resistance evaluation.

Efficient sleep classification based on entropy features and a support vector machine classifier.

Objective: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method based on entropy features and a support vector machine classifier, named SC-En&SVM.

Approach: Entropy features, including fuzzy measure entropy (FuzzMEn), fuzzy entropy, and sample entropy are applied for the analysis and classification of sleep stages.

Age-related network topological difference based on the sleep ECG signal.

Objective: Age has been shown to be a crucial factor for the EEG and fMRI small-world networks during sleep. However, the characteristics of the age-related network based on the sleep ECG signal and how the network changes during different sleep stages are poorly understood. This study focuses on exploring the age-related scale-free and small-world network properties of the ECG signal from male subjects during distinct sleep stages, including the wakeful (W), light sleep (LS), deep sleep (DS) and rapid eye movement (REM) stages.

Epidemiology of 45,616 suspect cases of Hand, Foot and Mouth Disease in Chongqing, China, 2011-2015.

Epidemiology and etiology of hand, foot, and mouth disease (HFMD) based on large sample size or evaluation of detection for more enterovirus serotypes are not well investigated in Chongqing of China. 45,616 suspect HFMD patients were prospectively enrolled among whom 21,615 were laboratory confirmed HFMD cases over a 5-year period (January 2011 to December 2015). Their epidemiological, clinical, and laboratory data were extracted and stratified by month, age, sex, disease severity, and enterovirus serotype.

[Multi-scale Permutation Entropy and Its Applications in the Identification of Seizures].

The electroencephalogram (EEG) has proved to be a valuable tool in the study of comprehensive conditions whose effects are manifest in the electrical brain activity, and epilepsy is one of such conditions. In the study, multiscale permutation entropy (MPE) was proposed to describe dynamical characteristics of EEG recordings from epilepsy and healthy subjects, then all the characteristic parameters were forwarded into a support vector machine (SVM) for classification. The classification accuracies of the MPE with SVM were evaluated by a series of experiments.

Analysis of alcoholic EEG signals based on horizontal visibility graph entropy.

This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics.

Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.

Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters.

Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal.

The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG).

Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm.

This paper proposes a fast weighted horizontal visibility graph constructing algorithm (FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated by comparing with Fast Fourier Transform (FFT) and sample entropy (SampEn) method. Two noise-robustness graph features based on the FWHVA, mean degree and mean strength, are investigated using two chaos signals and five groups of EEG signals.