ECG enhancement and QRS detection based on sparse derivatives

Xiaoran Ning, Ivan Selesnick

Research output: Contribution to journalArticle

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 ℓ1 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%.

Original languageEnglish (US)
Pages (from-to)713-723
Number of pages11
JournalBiomedical Signal Processing and Control
Volume8
Issue number6
DOIs
StatePublished - 2013

Fingerprint

Electrocardiography
Artifacts
Derivatives
Noise
Muscle Contraction
Cardiac Arrhythmias
Contamination
Databases

Keywords

  • ℓ norm optimization
  • Denoising
  • ECG enhancement
  • QRS detection
  • Sparse derivative

ASJC Scopus subject areas

  • Health Informatics
  • Signal Processing

Cite this

ECG enhancement and QRS detection based on sparse derivatives. / Ning, Xiaoran; Selesnick, Ivan.

In: Biomedical Signal Processing and Control, Vol. 8, No. 6, 2013, p. 713-723.

Research output: Contribution to journalArticle

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