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Algorithms to detect changes in cognitive load using non-invasive biosensors (e.g. electroencephalography (EEG)) have the potential to improve human-computer interactions by adapting systems to an individual's current information processing capacity, which may enhance performance and mitigate costly errors. However, for algorithms to provide maximal utility, they must be able to detect load across a variety of tasks and contexts. The current study aimed to build models that capture task-general EEG correlates of cognitive load, which would allow for load detection across variable task contexts.

Sliding-window support vector machines (SVM) were trained to predict periods of high versus low cognitive load across three cognitively and perceptually distinct tasks n-back, mental arithmetic, and multi-object tracking. To determine how well these SVMs could generalize to novel tasks, they were trained on data from two of the three tasks and evaluated on the held-out task. Additionally, to better understand task-continuously detect load across multiple task contexts.

EEG data contains task-general signatures of cognitive load. Sliding-window SVMs can capture these signatures and continuously detect load across multiple task contexts.

Portable devices for collecting electrocardiograms (ECGs) and telemedicine systems for diagnosis are available to residents in deprived areas, but ECGs collected by non-professionals are not necessarily reliable and may impair the accuracy of diagnosis. We propose an algorithm for accurate ECG quality assessment, which can help improve the reliability of ECGs collected by portable devices.

Using challenge data from CinC (2019), signals were classified as 'acceptable' and 'unacceptable' by annotators. The training set contained 998 12-lead ECGs and the test set contained 500. A 998 × 84 feature matrix, S, was formed by feature extraction and three basic models were obtained through training SVM, DT and NBC on S. The feature subsets S1, S2 and S3 were obtained by dimensionality reduction on S using SVM, DT and NBC, respectively. Three other basic models were obtained through training SVM on S1, DT on S2 and NBC on S3. By combining these six basic models, several integrated models were formed. An iterative method was proposed to select the integrated model with the highest accuracy on the training set. Having compared differences between the output labels and the original data labels, evaluation criteria were calculated.

An accuracy of 98.70% and 98.60% was achieved on the training and test datasets, respectively. High F1 score and Kappa values were also obtained.

The proposed algorithm has advantages over previously reported approaches during automatic assessment of ECG quality and can thus help to reduce reliance on highly trained professionals when assessing the quality of ECGs.

The proposed algorithm has advantages over previously reported approaches during automatic assessment of ECG quality and can thus help to reduce reliance on highly trained professionals when assessing the quality of ECGs.Lithium-ion capacitors (LICs) are now drawing increasing attention because of their potential to overcome the current energy limitations of supercapacitors and power limitations of lithium-ion batteries. In this work, we designed LICs by combining an electric double-layer capacitor cathode and a lithium-ion battery anode. Both the cathode and anode are derived from graphene-modified phenolic resin with tunable porosity and microstructure. They exhibit high specific capacity, superior rate capability and good cycling stability. Benefiting from the graphene-enhanced electrode materials, the all graphene-based LICs demonstrate a high working voltage (4.2 V), high energy density of 142.9 Wh kg-1, maximum power density of 12.1 kW kg-1 with energy density of 50 Wh kg-1, and long stable cycling performance (with ∼88% capacity retention after 5000 cycles). Considering the high performance of the device, the cost-effective and facile preparation process of the active materials, this all graphene-based lithium-ion capacitor could have many promising applications in energy storage systems.

Autonomic activity is possibly influenced by physical activity (PA). However, it remains unclear whether this association is modified by insulin resistance.

This population-based study between 2009 and 2012 included 2016 men and women aged 30-79years. The PA was assessed using a validated questionnaire based on sleep, occupation, transportation, household characteristics, and leisure-time PA. Heart rate (HR) and heart rate variability (HRV) in the sitting position were determined from 5-minute recordings of pulse waves detected by a fingertip sensor. The HRV was calculated as frequency (standard deviation of normal-to-normal [NN] intervals [SDNN]), root mean square of successive differences (RMSSD), and percentage differences between normal NN intervals >50milliseconds [pNN50]) and time domains. Insulin resistance was evaluated using the homeostasis model assessment index (HOMA-IR).

HR, RMSSD, and pNN50 were related to the total and moderate/vigorous PA tertiles in models that included HOMA-IR. The partial regression coefficient of total PA per 1-SD increase was .05 (P = .019) for log-transformed RMSSD and 1.86 (P = .001) for pNN50. No interactive associations were observed between PA and HOMA-IR.

Low total PA was associated with increased HR and low levels of RMSSD and pNN50, reflecting parasympathetic modulation that was not modified by insulin resistance.

Low total PA was associated with increased HR and low levels of RMSSD and pNN50, reflecting parasympathetic modulation that was not modified by insulin resistance.

Physical activity (PA) during pregnancy is associated with several benefits in maternal and child outcomes, and its relationship with preterm birth is still conflicting. This study aims to examine the associations between PA during pregnancy and occurrence of preterm birth.

PA was assessed by questionnaire (for each trimester) and accelerometry (second trimester) in women enrolled in a birth cohort study that started during pregnancy and included births that occurred between January 1 and December 31, 2015. Gestational age was based on the last menstrual period and ultrasonography. selleck kinase inhibitor All deliveries before 37weeks of gestation were considered preterm births. A Poisson regression model was used to measure associations controlling for potential confounders.

PA information was available for 4163 women and 13.8% of births were preterm. A total of 15.8% of women were engaged in PA during pregnancy. Multivariate analysis showed that only PA performed in the third trimester of pregnancy (prevalence ratio = 0.58; 95% confidence interval, 0.