Search

It is a hot problem in epilepsy research to detect and predict seizures by EEG signals. Clinically, it is generally observed that there are only sudden abnormal signals during the ictal stage, but there is no significant difference in the EEG signal between the interictal and preictal stages. To solve the problem that preictal signals are difficult to recognize clinically, and then effectively improve the recognition efficiency of epileptic seizures, so, in this paper, some nonlinear methods are comprehensively used to extract the hidden information in the EEG signals in different stages, namely, phase space reconstruction (PSR), Poincaré section (PS), synchroextracting transform (SET), and machine learning for EEG signal analysis. First, PSR based on C-C method is used, and the results show that there are different diffuse attractor trajectories of the signals in different stages. Second, the confidence ellipse (CE) is constructed by using the scatter diagram of the corresponding trajectory on PS, and the aspect ratio and area of the ellipse are calculated. The results show that there is an interesting transitional phenomenon in preictal stage. To recognize ictal and preictal signals, time-frequency (TF) spectrums, which are processed by SET, are fed into the convolutional neural network (CNN) classifier. The accuracy of recognizing ictal and preictal signals reaches 99.7% and 93.7%, respectively. To summarize, our results based on nonlinear method provide new research ideas for seizure detection and prediction. NEW & NOTEWORTHY: Our results based on nonlinear method have better practical significance and clinical application value and improved the prediction efficiency of epileptic EEG signals effectively. This work provides direct insight into the application of these biomarkers for seizure detection and prediction. [ABSTRACT FROM AUTHOR]

Background: Minerals play an important role in human health, but their effect on urinary function remains controversial. The aim of this study was to assess the association between dietary intake of minerals (Ca, P, Mg, Fe, Zn, Cu, Na, K, Se) and urine flow rate (UFR)., Methods: We conducted a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES, 2009-2018) database. Multivariate regression and smooth curve fitting were used to investigate the association between dietary mineral intakes and UFR. Subgroup analyses and interaction tests were used to investigate whether this association was stable in the population., Results: Our study involving 10,229 representative adult NHANES participants showed an association between Mg intake and UFR in a linear regression model for continuous variables. And in the model analysis of tertile categorical variables, we observed a positive association between six mineral intakes (Ca, Mg, Zn, Cu, Na, and K) and UFR. Smoothed curve fitting and threshold effect analysis further support the nonlinear relationship between mineral intakes and UFR. Subgroup analyses and interaction tests ensured the reliability and robustness of the findings., Conclusion: This study examined the effects of nine dietary minerals on UFR and found that intake of Ca, Mg, Zn, Cu, Na, and K were positively correlated with UFR, suggesting that these minerals may have a positive effect on improving urinary function. In particular, Mg showed a more significant positive correlation with UFR in women, while Na showed a stronger positive correlation in diabetics. However, P, Fe and Se did not show significant correlations. In summary, although these findings provide a preliminary understanding of the relationship between dietary minerals and urinary function, further prospective studies are still necessary to validate these relationships and explore the physiologic mechanisms underlying them., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Zhang, Ding and Gao.)