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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network.
Front Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
View Article and Find Full Text PDFPerformance of the pooled cohort equations and D:A:D risk scores among individuals with HIV in a global cardiovascular disease prevention trial: a cohort study leveraging data from REPRIEVE.
Lancet HIV
January 2025
Division of Cardiology, Duke University School of Medicine, Durham, NC, USA.
Background: Risk estimation is an essential component of cardiovascular disease prevention among people with HIV. We aimed to characterise how well atherosclerotic cardiovascular disease (ASCVD) risk scores used in clinical guidelines perform among people with HIV globally.
Methods: In this prospective cohort study leveraging REPRIEVE data, we included participants aged 40-75 years, with low-to-moderate traditional cardiovascular risk, not taking statin therapy.
Updating the Scottish national cardiovascular risk score: ASSIGN version 2.0.
Heart
January 2025
The George Institute for Global Health, School of Public Health, Imperial College London, London, UK.
Background: The Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network (ASSIGN) risk score, developed in 2006, is used in Scotland for estimating the 10-year risk of first atherosclerotic cardiovascular disease (ASCVD). Rates of ASCVD are decreasing, and an update is required. This study aimed to recalibrate ASSIGN (V.
View Article and Find Full Text PDFMulti-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.
Neural Netw
January 2025
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China. Electronic address:
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection.
View Article and Find Full Text PDFFrom April 2020 to December 2021, the Canadian federal government earmarked $330,000,000 through the Emergency Food Security Fund to address food insecurity during the COVID-19 global pandemic. These funds were disbursed through a handful of national and regional emergency food and food justice agencies to smaller front-line organizations for the purchase of emergency food provisions and personal protective equipment, and to hire additional workers. We theorize these dynamics within the broader processes of neoliberalization and argue that the Canadian federal government was conscripting food justice and community development organizations into its efforts to address dramatically increasing rates of food insecurity across the country through charity emergency food provisioning.
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