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. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

[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. It is indicated that the dynamical characteristics of EEG data with MPE could identify the differences among healthy, interictal and ictal states, and there was a reduction of MPE of EEG from the healthy and interictal state to the ictal state. Experimental results demonstrated that average classification accuracy was 100% by using the MPE as a feature to characterize the healthy and seizure, while 99. 58% accuracy was obtained to distinguish the seizure-free and seizure EEG. In addition, the single-scale permutation entropy (PE) at scales 1-5 was put into the SVM for classification at the same time for comparative analysis. The simulation results demonstrated that the proposed method could be a very powerful algorithm for seizure prediction and could have much better performance than the methods hased on single sale PF

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. To meet those requirements, an electromagnetic imaging (EMI) device, which is portable, non-invasive, and non-ionizing, has been developed. It uses a headset with an antenna array that irradiates the head with a safe low-frequency EM field and captures scattered fields to map the brain using a complementary set of physics-based and data-driven algorithms, enabling quasi-real-time detection, two-dimensional localization, and classification of strokes. This study reports clinical findings from the first time the device was used on stroke patients. The clinical results on 50 patients indicate achieving an overall accuracy of 98% in classification and 80% in two-dimensional quadrant localization. With its lightweight design and potential for use by a single para-medical staff at the point of care, the device can be used in intensive care units, emergency departments, and by paramedics for onsite diagnosis.

Impact of clean air action on the PM2.5 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 PM2.5, SO2, NO2, NH3, O3 and CO concentrations decreased by 40.9%, 46.0%, 29.0%, 40.6%, 11.0% and 44.4%, respectively. Significant decreases were also observed for NO3- (32.5%), SO42- (52.9%), NH4+ (56.0%), Cl- (64.6%) and K+ (68.2%), on average. Enhanced PM2.5 pollution has changed from sulfate-driven to nitrate-driven. The decrease in SO2 was more significant than NO2 as a response to one reason of the larger decrease in SO42- concentration. The formation of sulfate was dominated by heterogeneous reactions in two heating seasons. Low pH could facilitate more efficient conversion of SO2 to sulfate. Photochemical reactions played a much more important role in the formation of nitrate in the second heating season, especially in the daytime. The major source regions for sulfate and nitrate were identified by back trajectories and the potential source function (PSCF). More nitrate was brought into Beijing when air masses coming from polluted regions in the southwest prevailed in 2017-2018 heating season. Thus, regional joint prevention and control are of great importance in the achievement of an effective reduction in PM2.5 pollution in the future.

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

Introduction: 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. Methods: A prospective convenience study enrolled patients (January 2020 to August 2020) with known stroke to have brain electromagnetic imaging, in addition to usual imaging and medical care. The images are obtained by processing signals from encircling transceiver antennae which emit and detect low energy signals in the microwave frequency spectrum between 0.5 and 2.0 GHz. The purpose of the study was to refine the imaging algorithms. Results: Examples are presented of haemorrhagic and ischaemic stroke and comparison is made with CT, perfusion and MRI T2 FAIR sequence images. Conclusion: Due to speed of imaging, size and mobility of the device and negligible environmental risks, development of electromagnetic scanning scanner provides a promising additional modality for mobile and bedside neuroimaging.

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. Shenzhen was a highly endemic area of HBV throughout the study period (prevalence 9.69%). HBV infections were almost entirely in the 20 and older age groups with a male-to-female ratio of 1.16:1 which is approximately the same as the male-to-female ratio of the general population in China. However, only 71.25% of the general population retained HBV immune protection. Genotype B and C were identified as the most common agents; recombinant B/C and B/D also existed; some cases, however, could not be genotyped. NAs resistant mutation occurrence patterns were multitudinous; single mutation patterns of rtM204I/V and rtL180M occurrences accounted for majority, followed by the combinational mutation pattern L180M + M204I/V. Drug-resistance was prevalent, mainly occurring in the cross resistance patterns LAM + LdT and LAM + LdT + ETV, and significantly more critical in males. These results demonstrate that all people free from HBV infection should obtain injections of the vaccine or booster shots, and conventional virologic detection in a clinical laboratory center should incorporate genotype and mutation alongside the serological factors for etiology and develop better classification methods, such as sequencing.

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. APPROACH: The subjects are divided into two age groups: a younger (age ⩽ 40, n = 11) group and an older group (age > 40, n = 25). MAIN RESULTS: For the scale-free network analysis, our results reveal a distinctive pattern of the scale free network topologies between the two age groups, including the mean degree ([Formula: see text]), the clustering coefficient ([Formula: see text]), and the path length ([Formula: see text]) features, such as the slope distribution of [Formula: see text] in the younger group increased from 1.99 during W to above 2.05 during DS. In addition, the results indicate that the small-world properties can be found across all sleep stages in both age groups. However, the small-world index in the LS and REM stages significantly decreased with age (p = 0.0006 and p = 0.05, respectively). SIGNIFICANCE: The comparison analysis result indicates that the network topology variations in the sleep ECG signals are prone to show age-relevant differences that could be used for sleep stage classification and sleep disorder diagnosis.

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. FuzzyMEn has been used for heart rate variability analysis since it was proposed, while this is the first time it has been used for sleep scoring. The three features are extracted from 6 376 730 s epochs from Fpz-Cz electroencephalogram (EEG), Pz-Oz EEG and horizontal electrooculogram (EOG) signals in the sleep-EDF database. The independent samples t-test shows that the entropy values have significant differences among six sleep stages. The multi-class support vector machine (SVM) with a one-against-all class approach is utilized in this specific application for the first time. We perform 10-fold cross-validation as well as leave-one-subject-out cross-validation for 61 subjects to test the effectiveness and reliability of SC-En&SVM. MAIN RESULTS: The 10-fold cross-validation shows an effective performance with high stability of SC-En&SVM. The average accuracy and standard deviation for 2-6 states are 97.02 ± 0.58, 92.74 ± 1.32, 89.08 ± 0.90, 86.02 ± 1.06 and 83.94 ± 1.61, respectively. While for a more practical evaluation, the independent scheme is further performed, and the results show that our method achieved similar or slightly better average accuracies for 2-6 states of 94.15%, 85.06%, 80.96%, 78.68% and 75.98% compared with state-of-the-art methods. The corresponding kappa coefficients (0.81, 0.74, 0.72, 0.71, 0.67) guarantee substantial agreement of the classification. SIGNIFICANCE: We propose a novel sleep stage scoring method, SC-En&SVM, with easily accessible features and a simple classification algorithm, without reducing the classification performance compared with other approaches.

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. Subsequently 292 non-EV-A71/CV-A16 HFMD confirmed cases were randomly selected in three consecutive outbreaks to detect CV-A6 and CV-A10, using RT-PCR. Results showed that the HFMD epidemic peaked in early summer and autumn. The median age of onset was 2.45 years with a male-to-female ratio of 1.54:1, and with children under 5 years of age accounting for 92.54% of all confirmed cases. EV-A71 and CV-A16 infection accounted for only 36.05% (7793/21615) of total confirmed cases while EV-A71 accounted for 59.64% (232/389) of severe cases. Importantly, the proportion of EV-A71 infection generally increased with age which showed rapid growth in severe cases. CV-A6 and CV-A10 were tested positive in Chongqing, but CV-A6 had greater positive rates of 62.33% while CV-A10 had 4.79% in non-EV-A71/CV-A16 HFMD confirmed cases.

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). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P (k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of P (k) from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.

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. Experimental results show that feature extraction using the FWHVA is faster than that of SampEn and FFT. And mean strength feature associated with ictal EEG is significant higher than that of healthy and inter-ictal EEGs. In addition, an 100% classification accuracy for identifying seizure from healthy shows that the features based on the FWHVA are more promising than the frequency features based on FFT and entropy indices based on SampEn for time series classification.

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. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, [Formula: see text]1, [Formula: see text]3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 [Formula: see text] with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 [Formula: see text]. In contrast, SaE features associated cannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 [Formula: see text] even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.