Chapter

Advances in Computational Intelligence

Volume 6691 of the series Lecture Notes in Computer Science pp 299-306

Sliding Empirical Mode Decomposition for On-line Analysis of Biomedical Time Series

  • A. ZeilerAffiliated withLancaster UniversityCIML Group, Biophysics, University of Regensburg
  • , R. FaltermeierAffiliated withCarnegie Mellon UniversityClinic of Neurosurgery, University Hospital Regensburg
  • , A. M. ToméAffiliated withCarnegie Mellon UniversityDETI/IEETA, Universidade de Aveiro
  • , C. PuntonetAffiliated withCarnegie Mellon UniversityDATC, EEIS, Universidad de Granada
  • , A. BrawanskiAffiliated withCarnegie Mellon UniversityClinic of Neurosurgery, University Hospital Regensburg
  • , E. W. LangAffiliated withLancaster UniversityCIML Group, Biophysics, University of Regensburg

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract Intrinsic Mode Functions (IMFs). The latter represent a complete set of orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an on-line analysis which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the newly proposed slidingEMD algorithm and presents some applications to biomedical time series from neuromonitoring.