Automatic Threshold Determination for a Local Approach of Change Detection in Long-Term Signal Recordings
- PDF / 1,125,370 Bytes
- 7 Pages / 600.03 x 792 pts Page_size
- 31 Downloads / 166 Views
Research Article Automatic Threshold Determination for a Local Approach of Change Detection in Long-Term Signal Recordings ˆ 1 and David Hewson1 Wassim El Falou,1, 2 Mohamad Khalil,2 Jacques Duchene, 1 Institut 2 Facult´ e
des Sciences et Technologies de l’Information, Universit´e de Technologie de Troyes, France de G´enie I, Universit´e Libanaise, Tripoli, Lebanon
Received 18 October 2006; Revised 26 January 2007; Accepted 27 April 2007 Recommended by Gloria Menegaz 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. Copyright © 2007 Wassim El Falou et al. 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.
1.
INTRODUCTION
Change detection and segmentation are the first steps of many signal processing applications (see, e.g., speech processing [1–4], video tracking [5], ergonomics [6], biomedical applications [7–9], seismic applications [10]). Most detection and segmentation algorithms are based on the theory of statistical detection and hypothesis testing [10–12]. In such an approach, a change occurs when the statistical properties of the signal are modified. Roughly speaking, this can be expressed either by a different distribution function before and after the change time, or by a modification of the parameter value of the same distribution. For the latter case, when the parameter values are a priori known, an efficient algorithm to solve the detection problem is the CUSUM (cumulative sum) algorithm based on the log-likelihood ratio [10, 13]. CUSUM algorithm is optimal in the sense that it optimizes the worst detection delay when the mean time between false alarms goes to infinity [10]. In many applications, modifications can affect energy, frequency, or both [14, 15]. Detection of a change in the frequency content can be performed using the CUSUM algorithm applied on the innovation of an AR (au
Data Loading...
