http://rmib.mx/index.php/rmib/issue/feed Mexican Journal of Biomedical Engineering 2020-06-13T00:14:15+00:00 Prof. Cesár Antonio González Díaz rib.somib@gmail.com Open Journal Systems <center> <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>Call for Papers for Special Issue on “Biomedical Engineering Innovations for Coronavirus COVID-19”</p> </div> </div> </div> <p><a href="Call%20for Papers for Special Issue on “Biomedical Engineering Innovations for Coronavirus COVID-19”"><strong>DOWNLOAD FULL INFO HERE</strong></a></p> <p><strong>MISSION</strong></p> <p align="left"><em>La Revista Mexicana de Ingeniería Biomédica</em> (The Mexican Journal of Biomedical Engineering, RMIB, for its Spanish acronym) is a publication oriented to the dissemination of papers of the Mexican and international scientific community whose lines of research are aligned to the improvement of the quality of life through engineering techniques.</p> <p align="left">The papers that are considered for being published in the RMIB must be original, unpublished, and first rate, and they can cover the areas of Medical Instrumentation, Biomedical Signals, Medical Information Technology, Biomaterials, Clinical Engineering, Physiological Models, and Medical Imaging as well as lines of research related to various branches of engineering applied to the health sciences.</p> <p align="left">The RMIB is an electronic journal published quarterly ( January, May, September) by the Mexican Society of Biomedical Engineering,&nbsp; founded since 1980. It publishes articles in spanish and english and is aimed at academics, researchers and professionals interested in the subspecialties of Biomedical Engineering.</p> <p><strong>INDEXES</strong></p> <p><em>La revista Mexicana de Ingeniería Biomédica</em> is a quarterly publication, and it is found in the following indexes:</p> <p>&nbsp;<img src="/public/site/images/administrador/21.jpg" alt="" width="780" height="110"><img src="/public/site/images/administrador/1.jpg" alt="" width="780" height="110"><img src="/public/site/images/administrador/4.jpg" alt="" width="780" height="110"></p> <p><img src="/public/site/images/administrador/Unknown1.png" alt=""></p> </center> http://rmib.mx/index.php/rmib/article/view/1067 Contenido Vol. 41 No. 2 (2020) 2020-05-15T04:18:04+00:00 Coordinador Editorial rib.somib@gmail.com <p>- Información general</p> <p>- Comité Editorial</p> <p>- Mesa Directiva</p> <p>- Índice</p> 2020-05-15T00:00:00+00:00 Copyright (c) http://rmib.mx/index.php/rmib/article/view/1065 Editor's Letter 2020-05-15T04:20:39+00:00 César A. González Díaz rib.somib@gmail.com <p>La Revista Mexicana de Ingeniería Biomédica (The Mexican Journal of Biomedical Engineering), órgano oficial de divulgación de la Sociedad Mexicana de Ingeniería Biomédica, se ha consolidado como una de las mejores revistas científica de su campo en América Latina, tal logro es el resultado de 41 años de un esfuerzo continuo de muchas personas.</p> 2020-05-01T00:00:00+00:00 Copyright (c) http://rmib.mx/index.php/rmib/article/view/1030 Development and Simulation of an Automated Control Algorithm for Insulin Therapy of Hyperglycemic Emergencies in Diabetes 2020-05-15T04:45:21+00:00 Jared Becerril Rico jared.becerril.rico@nube.unadmexico.mx <p>El presente trabajo describe el desarrollo y simulación de un algoritmo para el control automático de la infusión de insulina en del manejo glucémico de pacientes con cetoacidosis diabética (CAD) y estado hiperosmolar hiperglucémico (EHH). Se programó un software que calcula la insulina necesaria para un descenso glucémico de 50mg/dL/h hasta llegar a glucemias de 250mg/dL, para posteriormente mantenerlas en 220mg/dL hasta la remisión de la patología. La simulación del software se realizó haciendo uso de registros glucémicos de 10 pacientes con CAD manejados en el Hospital Juárez de México. Los resultados de la simulación mostraron una menor incidencia de hipoglucemias, así como un menor requerimiento de insulina dentro del tratamiento, sin diferencias entre los descensos medios de glucosa por hora de las mediciones reales y simuladas. Este software propone un uso innovador de los llamados páncreas artificiales al aplicarlos en urgencias hiperglucémicas, implementando además el uso de la sensibilidad a la insulina como variable para el funcionamiento de los mismos. Los resultados demuestran que el algoritmo podría ser capaz de lograr un manejo glucémico apegado a las guías de tratamiento, generando un menor gasto de insulina y evitando hipoglucemias durante la terapéutica, con una posible aplicación en dispositivos biomédicos autónomos.</p> 2020-05-15T02:58:29+00:00 Copyright (c) 2020 Jared Becerril Rico http://rmib.mx/index.php/rmib/article/view/943 Regularized Hypothesis Testing in Random Fields with Applications to Neuroimaging 2020-06-13T00:14:15+00:00 Oscar S. Dalmau-Cedeño dalmau@cimat.mx Dora E. Alvarado-Carrillo dora.alvarado@cimat.mx José Luis Marroquín jlm@cimat.mx <p>The task of determining for which elements of a random field (e.g., pixels in an image) a certain null hypothesis may be rejected is a relevant problem in several scientific areas. In the current contribution, we introduce a new method for performing this task, the regularized hypothesis testing (RHT) method, focusing on its use in neuroimaging re- search. RHT is based on the formulation of the hypothesis testing task as a Bayesian estimation problem, with the previous application of a Markovian random field. The latter allows for the incorporation of local spatial informa- tion and considers different noise models, including spatially correlated noise. In tests on synthetic data showing regular activation levels on uncorrelated noise fields, RHT furnished a true positive rate (TPR) of 0.97, overcoming the state-of-the-art morphology-based hypothesis testing (MBHT) method and the traditional family-wise error rate (FWER) method, which afforded 0.93 and 0.58, respectively. For fields with highly correlated noise, the TPR provided by RHT was 0.65, and by MBHT and FWER was 0.35 and 0.29, respectively. For tests utilizing real func- tional magnetic resonance imaging (fMRI) data, RHT managed to locate the activation regions when 60% of the original signal were removed, while MBHT located only one region and FWER located none.</p> 2020-06-13T00:02:50+00:00 Copyright (c) 2020 Oscar Dalmau