Open Access

Automatic Threshold Determination for a Local Approach of Change Detection in Long-Term Signal Recordings

  • Wassim El Falou1, 2Email author,
  • Mohamad Khalil2,
  • Jacques Duchêne1 and
  • David Hewson1
EURASIP Journal on Advances in Signal Processing20072007:024748

https://doi.org/10.1155/2007/24748

Received: 18 October 2006

Accepted: 27 April 2007

Published: 6 June 2007

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.

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Authors’ Affiliations

(1)
Institut des Sciences et Technologies de l'Information, Université de Technologie de Troyes
(2)
Faculté de Génie I, Université Libanaise

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Copyright

© Wassim El Falou et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.