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 journalArticlepeer-review

Abstract

The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary 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 contribution, we applied the idea of EMD to develop strategies to automatically identify the relevant IMFs that contribute to the slow-varying trend in the data, and presented its application on the analysis of esophageal manometric time series in gastroesophageal reflux disease. The results from both extensive simulations and real data show that the EMD may prove to be a vital technique for the analysis of esophageal manometric data.

Original languageEnglish (US)
Pages (from-to)1692-1701
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume52
Issue number10
DOIs
StatePublished - Oct 2005

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering

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