Journal article
Application of the Empirical Mode Decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease
Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2006, pp 620-623
2004
PMID: 17271753
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 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.
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Details
- Title
- Application of the Empirical Mode Decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease
- Creators
- Hualou Liang - Sch. of Health Inf. Sci., Texas Univ., Houston, TX 77030, USAQiu-Hua LinJ D Z Chen
- Publication Details
- Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2006, pp 620-623
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000225461800160
- Other Identifier
- 991014877991004721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Engineering, Multidisciplinary
- Medicine, Research & Experimental
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging