Epilepsy is one of the most common neurological disorders generally characterized by sudden and recurrent unprovoked seizures, which is commonly diagnosed through visual inspection of electroencephalography (EEG) signals. However, since EEG signals are highly complex, nonlinear and nonstationary in nature, both visual inspection and the existing computed aid detection approaches fail to capture the intrinsic dynamics of seizure events, leading to unsatisfactory detection performance. Therefore, an accurate computer-aided automatic epileptic seizure diagnosis system is valuable and urgently needed in clinical application. In this study, we propose an automatic classification method using nonlinear dynamics features and nonlinear classifiers for determining whether the EEG signals are in interictal or ictal activities, respectively. The main novelty of this paper is that investigating the performance of discernable entropybased nonlinear dynamics features used for automatic diagnosis of an epileptic seizure. First, the discrete wavelet transform is applied to obtain de-noised and sub-band signals. Second, the nonlinear dynamics features are extracted based on information entropy theory. Subsequently, one-way analysis of variance and forward sequential feature selection technique are employed to identify the most explainable features for seizure detection. Then the selected features are fed to six different classifiers, namely, least squaresupport vector machine (LS-SVM), K-nearest neighbors, logistic regression, linear discriminant analysis, Naive Bayes classifier, and random forest, for automatic diagnosis of the epileptic seizure. Finally, a public EEG dataset from Bonn University, Germany, is utilized to investigate the performance of the proposed method. The experimental results show that the LS-SVM yields the optimal performance with an accuracy of 99.50%, a sensitivity of 100.00%, and a specificity of 99.40%. Our proposed method shows great potential for real-time diagnosis of the epileptic seizures, which can be deployed in hospitals to assist clinicians in accurate diagnosis of seizures.

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Nowadays, air pollution is a severe environmental problem in China. To investigate the effects of ambient air pollution on health, a time series analysis of daily outpatient and inpatient visits in 2015 were conducted in Shenzhen (China). Generalized additive model was employed to analyze associations between six air pollutants (namely SO2, CO, NO2, O3, PM10, and PM2.5) and daily outpatient and inpatient visits after adjusting confounding meteorological factors, time and day of the week effects. Significant associations between air pollutants and two types of hospital visits were observed. The estimated increase in overall outpatient visits associated with each 10 µg/m3 increase in air pollutant concentration ranged from 0.48% (O3 at lag 2) to 11.48% (SO2 with 2-day moving average); for overall inpatient visits ranged from 0.73% (O3 at lag 7) to 17.13% (SO2 with 8-day moving average). Our results also suggested a heterogeneity of the health effects across different outcomes and in different populations. The findings in present study indicate that even in Shenzhen, a less polluted area in China, significant associations exist between air pollution and daily number of overall outpatient and inpatient visits.

The feature extraction from electroencephalogram (EEG) signals is widely used for computer‐aided epileptic seizure detection. However, multiple channels of EEG signals and their correlations have not been completely harnessed. In this article, a novel automatic seizure detection approach is proposed by analyzing the spatiotemporal correlation of multi‐channel EEG signals. This approach combines the maximum cross‐correlation, robust‐principal component analysis, and least square‐support vector machine to detect the events. Our proposed method delivers higher detection sensitivity, specificity, and accuracy than the state‐of‐the‐art approaches based on the 19 channels’ EEG signals of 37 absence epilepsy patients experiencing 57 seizure events.

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