0000000000001755
AUTHOR
Azeddine Mjahad
Análisis de señales biomédicas para aplicación de terapias en la fibrilación ventricular cardiaca
La muerte súbita es una muerte natural, inesperada y rápida en un tiempo límite de 24 horas después del comienzo de un proceso patológico. Las causas más comunes de la muerte súbita son las enfermedades Cardiovasculares (ECV) que resultan estar entre las principales causas de muerte en todo el mundo. En 2012, la Organización Mundial de la Salud (OMS) registró 17,5 millones de muertes por ECV, que representan el 31 % de todas las muertes registradas en el mundo . Una de las enfermedades cardiovasculares con mayor mortalidad es la Fibrilación Ventricular (FV), que es una arritmia cardíaca producida por una actividad eléctrica desorganizada del corazón. Durante la FV, los ventrículos se contra…
Web Monitoring System and Gateway for Serial Communication PLC
Abstract An industrial process requires interacting with the rest of the plant, being able to exchange data with other devices and monitoring systems in order to optimize production, reporting information and providing control capabilities to distant users. Internet, and, especially web browsers are an excellent tool to provide information for remote users, allowing not only monitoring but also controlling the industrial process as an SCADA software or HMI system. The proposed system does not need specific proprietary software and its associated license costs. In this work, a webserver system is implemented under a Freescale microcontroller, acting as a gateway for a simple PLC with single …
Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis
A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from…
Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks
Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) d…
ECG Analysis for Ventricular Fibrillation Detection Using a Boltzmann Network
Arrhythmias consist on electrical alterations in the heart beat control. They can be identified by means of surface ECG leads. The main goal of this work is to provide a signal classification based on ECG signal waveform in the time-frequency domain especially targeted to Ventricular Fibrillation detection. The use of a classifier based on a Boltzmann network is proposed. However, a previous signal preprocessing is also required so that the Boltzmann network is fed with the appropriate data. In this case, an R-wave detector is used; after that, the Pseudo Wigner-Ville time-frequency distribution is obtained. This distribution is used to train and test the network, which handles it as an ima…
Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction
[ES] Este trabajo propone la detección de FV y su discriminación de TV y otros ritmos cardiacos basándose en la representación tiempo-frecuencia del ECG y su conversión en imágen como entrada a un clasificador de vecinos más cercanos (KNN) sin necesidad de extracción de parámetros adicionales. Tres variantes de datos de entrada al clasificador son evaluados. Los resultados clasifican la señal en cuatro clases diferentes: ’Normal’ para latidos con ritmo sinusal, ’FV’ para fibrilación ventricular, ’TV’ para taquicardia ventricular y ’Otros’ para el resto de ritmos. Los resultados para detección de FV mostraron 88,27% de sensibilidad y 98,22% de especificidad para la entrada de imágen equivale…
Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction
Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, …
Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitori…