Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10368434 | Biomedical Signal Processing and Control | 2013 | 11 Pages |
Abstract
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 â1 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%.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Xiaoran Ning, Ivan W. Selesnick,