Key result
The proposed real-time automatic ECG diagnosis algorithm achieved 99.37% sensitivity and 99.68% specificity for QRS detection on the MIT-BIH database, providing energy-efficient performance.
Why the study?
Does an embedded real-time automatic ECG diagnosis algorithm provide comparable performance to classical methods for pervasive cardiac care?
Does an embedded real-time automatic ECG diagnosis algorithm provide comparable performance to classical methods for pervasive cardiac care?
An embedded real-time automatic ECG diagnosis algorithm provides comparable performance to classical methods while being energy efficient for pervasive cardiac care.
Answers are grounded in this paper and cite their sources.
Supports energy-efficient real-time QRS detection in wearables; leaves open prospective clinical validation for pervasive cardiac care.
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.
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Zhou et al. (2009) studied Cardiac arrhythmias (n=30). Real-time Automatic ECG Diagnosis (AED) algorithm was evaluated on QRS detection sensitivity (MIT-BIH database). The proposed real-time automatic ECG diagnosis algorithm achieved 99.37% sensitivity and 99.68% specificity for QRS detection on the MIT-BIH database, providing energy-efficient performance.
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