Abstract
Blind Electrogastrogram (EGG) signal separation technology by using neural network-based independent component analysis (ICA) is presented in this paper. The experimental results show that using this technology, the true EGG components can be separated from the multi-channel EGG data contaminated by measurement artefacts, such as respiratory, motion, elcctrocardiogram (ECG) and so on, even though no prior information on such contaminating can be obtained, which is closer to practical situations in clinic applications.
Original language | English (US) |
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Pages (from-to) | 1351-1354 |
Number of pages | 4 |
Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
Volume | 3 |
State | Published - 1997 |
Externally published | Yes |
Event | Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA Duration: Oct 30 1997 → Nov 2 1997 |
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics