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Automatic threshold determination for a local approach of change detection in long-term signal recordings

  • David Hewson
    ,
  • Wassim El Falou
    ,
  • Mohamad Khalil
    ,
  • Jacques Duchêne
  • Université de technologie de Troyes
    ,
  • Lebanese University
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

CUSUM (cumulative sum) is a well-known method that can be used to detect changes in a signal when the parameters of this signal are known. This paper presents an adaptation of the CUSUM-based change detection algorithms to long-term signal recordings where the various hypotheses contained in the signal are unknown. The starting point of the work was the dynamic cumulative sum (DCS) algorithm, previously developed for application to long-term electromyography (EMG) recordings. DCS has been improved in two ways. The first was a new procedure to estimate the distribution parameters to ensure the respect of the detectability property. The second was the definition of two separate, automatically determined thresholds. One of them (lower threshold) acted to stop the estimation process, the other one (upper threshold) was applied to the detection function. The automatic determination of the thresholds was based on the Kullback-Leibler distance which gives information about the distance between the detected segments (events). Tests on simulated data demonstrated the efficiency of these improvements of the DCS algorithm.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Journal (Volume, Issue Number)

Eurasip Journal on Advances in Signal Processing (Volume 2007)

Publication milestones

  • Accepted/In press - 27/04/2007
  • Published - 01/12/2007

Publication status

Published - 01/12/2007

ISSN

1687-6172

External Publication IDs

  • handle.net: 10547/623482
  • Scopus: 34347337437

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