Adaptive Trend Filtering for ECG Denoising and Delineation

Tom Trigano, Shlomi Talala, David Luengo

Research output: Contribution to journalArticlepeer-review


Standard recordings of electrocardiograhic signals are contaminated by a large variety of noises and interferences, which impair their analysis and the further related diagnosis. In this paper, we propose a method, based on compressive sensing techniques, to remove the main noise artifacts and to locate the main features of the pulses in the electrocardiogram (ECG). The motivation is to use Trend Filtering with a varying proximal parameter, in order to sequentially capture the peaks of the ECG, which have different functional regularities. The practical implementation is based on an adaptive version of the ADMM (alternating direction method of multiplier) algorithm. We present results obtained on simulated signals and on real data illustrating the validity of this approach, showing that results in peak localization are very good in both cases and comparable to state of the art approaches.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Issue number12
StateAccepted/In press - 1 Jan 2023


  • compressive sensing
  • delineation
  • denoising
  • ECG signal processing
  • Electrocardiography
  • Filtering
  • Market research
  • Noise reduction
  • Perturbation methods
  • Recording
  • Time-frequency analysis
  • trend filtering

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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