A mobile ECG monitoring system with context collection

Abstract

Preventative health management represents a shift from the traditional approach of reactive treatment-based healthcare towards a proactive wellness-management approach where patients are encouraged to stay healthy with expert support when they need it, at any location and any time. This work represents a step along the road towards proactive, preventative healthcare for cardiac patients. It seeks to develop a smart mobile ECG monitoring system that requests and records context information about what is happening around the subject when an arrhythmia event occurs. Context information about the subject's activities of daily living will, it is hoped, provide an enriched data set for clinicians and so improve clinical decision making. As a first step towards a mobile cardiac wellness guideline system, the authors present a system which can receive bio-signals that are wirelessly streamed across a body area network from Bluetooth enabled electrocardiographs. The system can store signals as they arrive while also responding to significant changes in Electrocardiogram activity. The authors have developed a prototype on a handheld computer that detects and responds to changes in the calculated heart rate as detected in an ECG signal. Although the general approach taken in this work could be applied to a wide range of bio-signals, the work focuses on ECG signals. The components of the system are, - A Bluetooth receiver, data collection and storage module - A real-time ECG beat detection algorithm. - An Event-Condition-Action (E-CA) rule base which decides when to request context information from the user. - A simple user interface which can request additional information from the user. A selection of real-time ECG detection algorithms were investigated for this work and one algorithm was tested in MATLAB and then implemented in Java. In order to collect ECG signals (and in principle any signals), the generalised data collection architecture has also been developed using Java and Bluetooth technology. An Event-Condition-Action (ECA) rule based expert system evaluates the changes in heart beat interval to decide when to interact with the user to request context information. © 2009 Springer Berlin Heidelberg.