Journal article
Extraction of gastric slow waves from electrogastrograms: combining independent component analysis and adaptive signal enhancement
Medical & biological engineering & computing, v 43(2), pp 245-251
Mar 2005
PMID: 15865135
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The electrogastrogram (EGG), a cutaneous measurement of gastric electrical activity, is a mixture of gastric slow waves and noise. To detect the propagation of gastric slow waves, it is desired to obtain gastric slow waves in each of multichannel EGGs. Recently, independent component analysis (ICA) has shown its efficiency in separating the gastric slow wave from noisy multichannel EGGs. However, this method is not able to recover gastric slow waves in each of the multichannel EGGs. In this paper, a two-stage combined method was proposed for extracting gastric slow waves. First, ICA was performed to separate the gastric slow wave component from noisy multichannel EGGs. Second, adaptive signal enhancement with a reference input derived by the ICA in the first stage was employed to extract gastric slow waves in each channel. Quantitative analysis showed that, with the proposed method, the maximum root-mean-square error between the estimated time lag and its theoretical value in the simulations was only 0.65. The results from real EGG data demonstrated that the combined method was able to extract gastric slow waves from individual channels of EGGs which are important to identify the slow wave propagation. Therefore, the proposed method can be used to detect propagation of gastric slow waves from multichannel EGGs.
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Details
- Title
- Extraction of gastric slow waves from electrogastrograms: combining independent component analysis and adaptive signal enhancement
- Creators
- H Liang - School of Health Information Sciences, University of Texas at Houston, Houston, USA. hualou.liang@uth.tmc.edu
- Publication Details
- Medical & biological engineering & computing, v 43(2), pp 245-251
- Publisher
- Springer Nature; United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000228803500012
- Scopus ID
- 2-s2.0-18144378847
- Other Identifier
- 991014878445404721
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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
- Mathematical & Computational Biology
- Medical Informatics