Transient Artifacts Suppression in Time Series via Convex Analysis

Yining Feng, Baoqing Ding, Harry Graber, Ivan Selesnick

Research output: Chapter in Book/Report/Conference proceedingChapter


This book chapter addresses the suppression of transient artifacts in time series data. We categorize the transient artifacts into two general types: spikes and brief waves with zero baseline, and step discontinuities. We propose a sparse-assisted optimization problem for the estimation of signals comprising a low-pass signal, a sparse piecewise constant signal, a piecewise constant signal, and additive white Gaussian noise. For better estimation of the artifacts, in turns better suppression performance, we propose a non-convex generalized conjoint penalty that can be designed to preserve the convexity of the total cost function to be minimized, thereby realizing the benefits of a convex optimization framework (reliable, robust algorithms, etc.). Compared to the conventional use of ℓ 1 norm penalty, the proposed non-convex penalty does not underestimate the true amplitude of signal values. We derive a fast proximal algorithm to implement the method. The proposed method is demonstrated on the suppression of artifacts in near-infrared spectroscopic (NIRS) measures.

Original languageEnglish (US)
Title of host publicationSignal Processing in Medicine and Biology
Subtitle of host publicationEmerging Trends in Research and Applications
PublisherSpringer International Publishing
Number of pages32
ISBN (Electronic)9783030368449
ISBN (Print)9783030368432
StatePublished - Jan 1 2020


  • Artifact reduction
  • Convex optimization
  • Fused lasso
  • Morphological component analysis
  • Non-convex regularization
  • Sparse signal processing

ASJC Scopus subject areas

  • General Engineering
  • General Biochemistry, Genetics and Molecular Biology
  • General Medicine
  • General Health Professions


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