Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease

Hualou Liang, Qiu Hua Lin, J. D.Z. Chen

Research output: Contribution to journalConference articlepeer-review

Abstract

The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and non-stationary time series. The central idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions (IMFs). An IMF is defined as any function having the number of extrema and the number of zero-crossings equal (or differing at most by one), and also having symmetric envelopes defined by the local minima, and maxima respectively. The decomposition procedure is adaptive, data-driven, therefore, highly efficient. In this paper, the EMD is first described, and its performance is validated by simulations. The EMD is then applied to the analysis of esophageal manometric time series in gastroesophageal reflux disease. The results show that the EMD may prove to be a vital technique for the analysis of esophageal manometric data.

Original languageEnglish (US)
Pages (from-to)620-623
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 I
StatePublished - Dec 1 2004
Externally publishedYes
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Keywords

  • Empirical mode decomposition
  • Esophageal manometry
  • Gastroesophageal reflux disease
  • Lower esophageal sphincter

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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