Browsing by Issue Date, starting with "2025-04-09"
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- Intra- and inter-regional complexity in multi-channel awake EEG through multivariate multiscale dispersion entropy for assessing sleep quality and agingPublication . Zandbagleh, Ahmad; Sanei, Saeid; Penalba-Sánchez, Lucía; Rodrigues, Pedro Miguel; Crook-Rumsey, Mark; Azami, HamedAging and poor sleep quality are associated with altered brain dynamics, yet current electroencephalography (EEG) analyses often overlook regional complexity. This study addresses this gap by introducing a novel integration of intra- and inter-regional complexity analysis using multivariate multiscale dispersion entropy (mvMDE) from awake resting-state EEG for the first time. Moreover, assessing both intra- and inter-regional complexity provides a comprehensive perspective on the dynamic interplay between localized neural activity and its coordination across brain regions, which is essential for understanding the neural substrates of aging and sleep quality. Data from 58 participants—24 young adults (mean age = 24.7 ± 3.4) and 34 older adults (mean age = 72.9 ± 4.2)—were analyzed, with each age group further divided based on Pittsburgh Sleep Quality Index (PSQI) scores. To capture inter-regional complexity, mvMDE was applied to the most informative group of sensors, with one sensor selected from each brain region using four methods: highest average correlation, highest entropy, highest mutual information, and highest principal component loading. This targeted approach reduced computational cost and enhanced the effect sizes (ESs), particularly at large scale factors (e.g., 25) linked to delta-band activity, with the PCA-based method achieving the highest ESs (1.043 for sleep quality in older adults). Overall, we expect that both inter- and intra-regional complexity will play a pivotal role in elucidating neural mechanisms as captured by various physiological data modalities—such as EEG, magnetoencephalography, and magnetic resonance imaging—thereby offering promising insights for a range of biomedical applications.
- Pressure injury prediction in intensive care units using artificial intelligence: a scoping reviewPublication . Alves, José; Azevedo, Rita; Marques, Ana; Encarnação, Rúben; Alves, PauloBackground/Objetives: Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals’ quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. Methods: The review followed the Joanna Briggs Institute’s methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. Results: This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. Conclusions: Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations.