Effects of COVID-19-related psychological distress and anxiety on quality of sleep and life in healthcare workers in Iran and three European countries.

Introduction: The COVID-19 pandemic has considerably affected human beings most of whom are healthcare workers (HCWs) combating the disease in the front line. Methods: This cross-sectional study aims to explore the effects of stress and anxiety caused by COVID-19 on the quality of sleep and life in HCWs, including physicians, nurses, and other healthcare staff. In this global study, we asked 1,210 HCWs (620 and 590 volunteers from Iran and European countries, including Germany, the Netherlands, and Italy, respectively), who age 21-70, to participate in the test. Several measures of COVID-related stress, anxiety, sleep, and life quality, including the 12-item General Health Questionnaire (GHQ-12), Fear of COVID-19 scale (FCV-19S), Beck Anxiety Inventory (BAI), the Pittsburgh Sleep Quality Index (PSQI), and World Health Organization Quality of Life-BREF (WHOQOL-BREF) are recorded. Results: Volunteers reported high rates of stress and anxiety and poor sleep quality as well as lower quality of life. The correlation analysis between the measures is reported. According to the results, regardless of the location, HCWs, predominantly female nurses, developed anxiety and stress symptoms which consequently resulted in lower sleep and life quality. Both for Iranian and the European HCWs, significant differences existed between nurses and the other two groups, with the p-values equal to 0.0357 and 0.0429 for GHQ-12, 0.0368, and 0.714 for BAI measure. Even though nurses reported the most stress, anxiety, fear of COVID-19, lower quality of life and sleep in both countries, and also an increase in other measures as well, there existed no statistically significant difference in FCV-19S, PSQI, and WHOQOL-BREF. Discussion: This study helps to expand our knowledge the effects of pandemics on HCWs and also for healthcare management to predict HCW's mental health conditions in similar situations.

EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms.

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.