The Suppression of Transient Artifacts in Time Series via Convex Analysis

Yining Feng, Harry Graber, Ivan Selesnick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For the suppression of transient artifacts in time series data, we propose a non-convex generalized fused lasso penalty for the estimation of signals comprising a low-pass signal, a sparse piecewise constant signal, and additive white Gaussian noise. The proposed non-convex penalty is designed so as 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 L1 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 publication2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538659168
DOIs
StatePublished - Jan 16 2019
Event2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Philadelphia, United States
Duration: Dec 1 2018 → …

Publication series

Name2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings

Conference

Conference2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018
CountryUnited States
CityPhiladelphia
Period12/1/18 → …

Fingerprint

Artifacts
Time series
Convex optimization
Cost functions
Infrared radiation
Costs and Cost Analysis
alachlor

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

Cite this

Feng, Y., Graber, H., & Selesnick, I. (2019). The Suppression of Transient Artifacts in Time Series via Convex Analysis. In 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings [8615601] (2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB.2018.8615601

The Suppression of Transient Artifacts in Time Series via Convex Analysis. / Feng, Yining; Graber, Harry; Selesnick, Ivan.

2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8615601 (2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Feng, Y, Graber, H & Selesnick, I 2019, The Suppression of Transient Artifacts in Time Series via Convex Analysis. in 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings., 8615601, 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018, Philadelphia, United States, 12/1/18. https://doi.org/10.1109/SPMB.2018.8615601
Feng Y, Graber H, Selesnick I. The Suppression of Transient Artifacts in Time Series via Convex Analysis. In 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8615601. (2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings). https://doi.org/10.1109/SPMB.2018.8615601
Feng, Yining ; Graber, Harry ; Selesnick, Ivan. / The Suppression of Transient Artifacts in Time Series via Convex Analysis. 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings).
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