Diagnóstico de las dificultades de lectura y escritura y de la dislexia basado en la Teoría PASS de lainteligencia utilizando la batería DN-CAS. Origen cognitivo de la dislexia

Las dificultades de lectura y escritura se pueden detectar desde el momento en que los niños y niñas inician el aprendizaje de la lectoescritura en la etapa de Educación Infantil. En algunos casos estos alumnos reciben un apoyo escolar que en muchas ocasiones no conlleva las mejoras esperadas, siendo las técnicas y metodologías de refuerzo aplicadas ineficaces. El problema, desde nuestro punto de vista, empieza con el diagnóstico que se realiza a estos jóvenes, que determina las directrices de la intervención idónea en cada caso. La Teoría PASS de la inteligencia nos permite conocer qué procesos están implicados cuando el niño lee o escribe, y parte de la premisa de que si conocemos el perfil cognitivo de un alumno que presenta dificultades podremos entender como estas se originan. Para conocer este perfil cognitivo (los cuatro procesos cognitivos que describe esta teoría: Planificación, Atención, Simultaneo y Secuencial) utilizamos la batería DN-CAS (Das & Naglieri: Cognitive Assessment System). El perfil obtenido al aplicar el DN-CAS nos permitirá conocer el origen de las dificultades de lectura y escritura, saber cuando está justificada una dislexia, descartar problemas emocionales o la presencia de los mismos y diseñar la intervención más adecuada en cada situación

Implantación de una plataforma corporativa de business intelligence

La Universidad UPROMA ha identificado una línea de actuación inicial caracterizada por la implantación de una plataforma de reporting y análisis de la información corporativa.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Differences of Functional Connectivity Brain Network in Emotional Judgment

Using combined emotional stimuli, combining photos of faces and recording of voices, we investigated the neural dynamics of emotional judgment using scalp EEG recordings. Stimuli could be either combioned in a congruent, or a non-congruent way.. As many evidences show the major role of alpha in emotional processing, the alpha band was subjected to be analyzed. Analysis was performed by computing the synchronization of the EEGs and the conditions congruent vs. non-congruent were compared using statistical tools. The obtained results demonstrate that scalp EEG ccould be used as a tool to investigate the neural dynamics of emotional valence and discriminate various emotions (angry, happy and neutral stimuli).

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.