Digitalización de imágenes de ECG para la detección del síndrome de Bayés

Bayes Syndrome is a recently recognized by medical community sickness. This desease has been studied in the last decades by its discoverer, MD. Antonio Bayés de Luna. Since several works shows that this desease is related to multiple symptoms, an early detection is considered relevant. Given that digital support of the EKG signal is mandatory for its analysis by a computer algorithm and considering that even with the technological advances, a big number of health institutions rely on paper or image digitalized support por EKGs. Hence, an image digitalization method that preserves the signal features that are relevant to diagnose the Bayes Syndrome is needed. In this paper, some alternatives of digitalization are analyzed for a representative dataset. Results are promising and shows that developed digitalization algorithm could be used for the further project stages that involves signal processing and classification.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Digitalización de imágenes de ECG para la detección del síndrome de Bayés

Bayes Syndrome is a recently recognized by medical community sickness. This desease has been studied in the last decades by its discoverer, MD. Antonio Bayés de Luna. Since several works shows that this desease is related to multiple symptoms, an early detection is considered relevant. Given that digital support of the EKG signal is mandatory for its analysis by a computer algorithm and considering that even with the technological advances, a big number of health institutions rely on paper or image digitalized support por EKGs. Hence, an image digitalization method that preserves the signal features that are relevant to diagnose the Bayes Syndrome is needed. In this paper, some alternatives of digitalization are analyzed for a representative dataset. Results are promising and shows that developed digitalization algorithm could be used for the further project stages that involves signal processing and classification.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Digitalización de imágenes de ECG para la detección del síndrome de Bayés

Bayes Syndrome is a recently recognized by medical community sickness. This desease has been studied in the last decades by its discoverer, MD. Antonio Bayés de Luna. Since several works shows that this desease is related to multiple symptoms, an early detection is considered relevant. Given that digital support of the EKG signal is mandatory for its analysis by a computer algorithm and considering that even with the technological advances, a big number of health institutions rely on paper or image digitalized support por EKGs. Hence, an image digitalization method that preserves the signal features that are relevant to diagnose the Bayes Syndrome is needed. In this paper, some alternatives of digitalization are analyzed for a representative dataset. Results are promising and shows that developed digitalization algorithm could be used for the further project stages that involves signal processing and classification.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Calibración de un algoritmo de detección de anomalías marítimas basado en la fusión de datos satelitales

La fusión de diferentes fuentes de datos aporta una ayuda significativa en el proceso de toma de decisiones. El presente artículo describe el desarrollo de una plataforma que permite detectar anomalías marítimas por medio de la fusión de datos del Sistema de Información Automática (AIS) para seguimiento de buques y de imágenes satelitales de Radares de Apertura Sintética (SAR). Estas anomalías son presentadas al operador como un conjunto de detecciones que requieren ser monitoreadas para descubrir su naturaleza. El proceso de detección se lleva adelante primero identificando objetos dentro de las imágenes SAR a través de la aplicación de algoritmos CFAR, y luego correlacionando los objetos detectados con los datos reportados mediante el sistema AIS. En este trabajo reportamos las pruebas realizadas con diferentes configuraciones de los parámetros para los algoritmos de detección y asociación, analizamos la respuesta de la plataforma y reportamos la combinación de parámetros que reporta mejores resultados para las imágenes utilizadas. Este es un primer paso en nuestro objetivo futuro de desarrollar un sistema que ajuste los parámetros en forma dinámica dependiendo de las imágenes disponibles.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI