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Recent advances in the field of cognitive sciences have provided evidence to classify activities with different cognitive engagement levels. High cognitive load and sustained attention may predispose an individual to psychological and physical stress and may result in a lack of performance. Cognitive load monitoring of professionals in high precision fields has gathered significant attention in the current research scenario. The proposed work in continuity has tried to demonstrate the use of two non‐invasive physiological sensing modalities viz. galvanic skin response (GSR) and photoplethysmography (PPG) to elucidate effectiveness in picking up differences of two induced cognitive state categories based on machine learning and statistical methods. This paper demonstrates how GSR and PPG signals data can be effectively utilized to examine the shift in cognitive load. The present work proposes the use of general linear chirplet transform (GLCT) to evaluate the time‐frequency characteristic of GSR and PPG signals and utilize the statistical features in classification. Random Forest, Decision Tree, and k‐Nearest Neighbours demonstrated an accuracy of 92.13%, 88.0%, and 86.13% respectively on a dataset of 20 subjects against an optimized feature set, thus demonstrating the effectiveness of the proposed methodology for differentiating pre‐defined categories of cognitive load. The study shows the potential of GSR and PPG signals attributes and time‐frequency representation using GLCT to monitor cognitive load in real‐life conditions. The proposed work also indicates the possibility to extend the same to attention‐demanding fields such as aviation, medicine, and manufacturing for effective remediation of fatigue periods with increased cognitive demand, which, if sustained, may lead to cognitive decline in the long run. [ABSTRACT FROM AUTHOR]

Background Recent evaluation of rheumatic heart disease (RHD) mortality demonstrates disproportionate disease burden within the United States. However, there are few contemporary data on US children living with acute rheumatic fever (ARF) and RHD. Methods and Results Twenty-two US pediatric institutions participated in a 10-year review (2008-2018) of electronic medical records and echocardiographic databases of children 4 to 17 years diagnosed with ARF/RHD to determine demographics, diagnosis, and management. Geocoding was used to determine a census tract-based socioeconomic deprivation index. Descriptive statistics of patient characteristics and regression analysis of RHD classification, disease severity, and initial antibiotic prescription according to community deprivation were obtained. Data for 947 cases showed median age at diagnosis of 9 years; 51% and 56% identified as male and non-White, respectively. Most (89%) had health insurance and were first diagnosed in the United States (82%). Only 13% reported travel to an endemic region before diagnosis. Although 96% of patients were prescribed secondary prophylaxis, only 58% were prescribed intramuscular benzathine penicillin G. Higher deprivation was associated with increasing disease severity (odds ratio, 1.25; 95% CI, 1.08-1.46). Conclusions The majority of recent US cases of ARF and RHD are endemic rather than the result of foreign exposure. Children who live in more deprived communities are at risk for more severe disease. This study demonstrates a need to improve guideline-based treatment for ARF/RHD with respect to secondary prophylaxis and to increase research efforts to better understand ARF and RHD in the United States.