A speedy cardiovascular diseases classifier using multiple criteria decision analysis

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented

Desarrollo de un sistema IoT integrado con dispositivos de eHealth para la detección automática de la variabilidad cardiaca

El presente trabajo final de grado tiene como objetivo principal el diseño e implementación de una solución portable para pacientes con problemas cardiovasculares relacionados con arritmias y trastornos en la frecuencia cardíaca. Dicha solución se constituirá a través de un sistema de sensorización, procesado de datos y comunicación que haga uso de las últimas tecnologías en el ámbito de la eHealth y el IoT, e implemente los estándares de interoperabilidad para garantizar un uso extendido a bajo coste.Palao Cruz, C. (2017). Desarrollo de un sistema IoT integrado con dispositivos de eHealth para la detección automática de la variabilidad cardiaca. http://hdl.handle.net/10251/91750TFG

Implementation of a Data Packet Generator Using Pattern Matching for Wearable ECG Monitoring Systems

In this paper, a packet generator using a pattern matching algorithm for real-time abnormal heartbeat detection is proposed. The packet generator creates a very small data packet which conveys sufficient crucial information for health condition analysis. The data packet envelopes real time ECG signals and transmits them to a smartphone via Bluetooth. An Android application was developed specifically to decode the packet and extract ECG information for health condition analysis. Several graphical presentations are displayed and shown on the smartphone. We evaluate the performance of abnormal heartbeat detection accuracy using the MIT/BIH Arrhythmia Database and real time experiments. The experimental result confirm our finding that abnormal heart beat detection is practically possible. We also performed data compression ratio and signal restoration performance evaluations to establish the usefulness of the proposed packet generator and the results were excellent