Neonatal jaundice is a medical condition which occurs in newborns as a result of an imbalance between the production and elimination of bilirubin. Excess bilirubin in the blood stream diffuses into the surrounding tissue leading to a yellowing of the skin. An optical system integrated with a signal processing system is used as a platform to noninvasively quantify bilirubin concentration through the measurement of diffuse skin reflectance. Initial studies, based on simulated skin reflectance spectra have lead to the generation of a clinical model for neonatal jaundice which, generates spectral reflectance data for jaundiced skin with varying levels of bilirubin concentration in the tissue. The spectral database built using the jaundice clinical model is then used as a test database to validate the signal processing system in real time. This evaluation forms the basis for understanding the translation of this research to human trials. The new jaundice clinical model and signal processing system have been successful validated using a porcine model as a surrogate for neonatal skin tissue. Samples of pig skin were soaked in bilirubin solutions of varying concentrations to simulate jaundice skin conditions. The resulting skins samples were analyzed with our skin reflectance systems producing bilirubin concentration values that show a high correlation (R(2) = 0.96) to concentration of the bilirubin solution that each porcine tissue sample was soaked in‥
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http://dx.doi.org/10.1109/IEMBS.2010.5626744 | DOI Listing |
Sci Rep
January 2025
Westchase Software, Houston, TX, 77063, USA.
It is well known that the sedimentary rock record is both incomplete and biased by spatially highly variable rates of sedimentation. Without absolute age constraints of sufficient resolution, the temporal correlation of spatially disjunct records is therefore problematic and uncertain, but these effects have rarely been analysed quantitatively using signal processing methods. Here we use a computational process model to illustrate and analyse how spatial and temporal geochemical records can be biased by the inherent, heterogenous processes of marine sedimentation and preservation.
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December 2024
The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou 510317, China. Electronic address:
Tuberculosis (TB) is the second deadliest infectious disease worldwide. Current TB diagnostics utilize sputum samples, which are difficult to obtain, and sample processing is time-consuming and difficult. This study developed an integrated diagnostic platform for the rapid visual detection of Mycobacterium tuberculosis (Mtb) in breath samples at the point-of-care (POC), especially in resource-limited settings.
View Article and Find Full Text PDFGait Posture
December 2024
Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China. Electronic address:
Background: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals.
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January 2025
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients.
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January 2025
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, California, USA.
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals.
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