Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 8, Issue 6, Pages 713-723Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2013.06.005
Keywords
ECG enhancement; QRS detection; l(1) norm optimization; Sparse derivative; Denoising
Categories
Funding
- NSF [CCF-1018020]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1018020] Funding Source: National Science Foundation
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Electrocardiography (ECG) signals are often contaminated by various kinds of noise or artifacts, for example, morphological changes due to motion artifact, non-stationary noise due to muscular contraction (EMG), etc. Some of these contaminations severely affect the usefulness of ECG signals, especially when computer aided algorithms are utilized. In this paper, a novel ECG enhancement algorithm is proposed based on sparse derivatives. By solving a convex optimization problem, artifacts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives (differences) are sparse respectively. The algorithm is applied to a QRS detection system and validated using the MIT-BIH Arrhythmia database (109,452 anotations), resulting a sensitivity of Se = 99.87% and a positive prediction of +P = 99.88%. (C) 2013 Elsevier Ltd. All rights reserved.
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