Abstract
Recent developments of the wireless sensor network will revolutionize the way of remote monitoring in dif-ferent domains such as smart home and smart care, particularly remote cardiac care. Thus, it is challenging to propose an energy efficient technique for automatic ECG diagnosis (AED) to be embedded into the wireless sensor. Due to the high resource requirements, classical AED methods are unsuitable for pervasive cardiac care (PCC) applications. This paper proposes an embedded real-time AED algorithm dedicated to PCC sys-tems. This AED algorithm consists of a QRS detector and a rhythm classifier. The QRS detector adopts the linear time-domain statistical and syntactic analysis method and the geometric feature extraction modeling technique. The rhythm classifier employs the self-learning expert system and the confidence interval method. Currently, this AED algorithm has been implemented and evaluated on the PCC system for 30 patients in the Gabriel Monpied hospital (CHRU of Clermont-Ferrand, France) and the MIT-BIH cardiac arrhythmias da-tabase. The overall results show that this energy efficient algorithm provides the same performance as the classical ones.
Highlights
Due to the increasing occurrence of sudden death events caused by cardiovascular diseases, there is a need to provide a long-term, real-time continuous pervasive cardiac care (PCC) service for the sudden death high-risk population
Due to the high resource requirements, classical automatic ECG diagnosis (AED) methods are unsuitable for pervasive cardiac care (PCC) applications
This paper proposes an embedded real-time AED algorithm dedicated to PCC systems
Summary
Due to the increasing occurrence of sudden death events caused by cardiovascular diseases, there is a need to provide a long-term, real-time continuous PCC service for the sudden death high-risk population. The PCC system has been developed for different populations at a variety of environment, including at home, clinical and outdoor. The studies of AED methods focused mainly on the clinical services. The acquisitions of the PCC system is ambulatory ECG signal that is non-stationary and easy-disturbed by interferences. The nodes of the PCC system have strict resource constraints, i.e. the capacities of computation, storage and power supply. Classical AED algorithms are unfit for the PCC system. This paper presents a real-time and low resource consumption AED algorithm for the PCC system.
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