Skip to search boxSkip to navigationSkip to main content

Intrinsic mode entropy for nonlinear discriminant analysis

  • David Hewson
    ,
  • Hassan Amoud
    ,
  • Hichem Snoussi
    ,
  • Michel Doussot
    ,
  • Jacques Duchêne
  • Université de technologie de Troyes
Research Output:
Contribution to journal
Article
 Peer-review

Abstract

Several methods of measuring entropy of time series have been developed and applied on physiological signals in order to distinguish data sets according to their underlying nonlinear dynamics. These methods are not well adapted for studying the time series in different scales, in the presence of dominant local trends and low-frequency components. In this letter, intrinsic mode entropy (IMEn) is proposed as an entropy measure over multiple oscillation levels. Robustness to local trends is ensured with this new measure, enabling an efficient characterization of the underlying nonlinear dynamics of the time series considered. IMEn is obtained by computing the Sample Entropy (SampEn) of the cumulative sums of the intrinsic mode functions extracted by the empirical mode decomposition method. An example of an application of IMEn is then presented, with the method able to successfully discriminate between two groups of subjects (elderly versus control) for signals of postural stability

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Journal (Volume, Issue Number)

IEEE Signal Processing Letters (Volume 14, Issue 5)

Publication milestones

  • Published - 16/04/2007

Publication status

Published - 16/04/2007

ISSN

1070-9908

Publication IDs

  • handle.net: 10547/623464
  • Scopus: 34247251291

Access to documents

Publication metrics