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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006
eBook Chapter
48 Modeling and Classification for Uterine EMG Signals Using Autoregressive Model
By
Mohamad O. Diab
,
Mohamad O. Diab
UMR CNRS 6600,
University of Technology of Compiègne
, Compiègne
, France
.Biomedical Department,
Islamic University of Lebanon
, Beirut
, Lebanon
.
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Catherine Marque
,
Catherine Marque
UMR CNRS 6600,
University of Technology of Compiègne
, Compiègne
, France
.
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Mohamad Khalil
Mohamad Khalil
Biomedical Department,
Islamic University of Lebanon
, Beirut
, Lebanon
.
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Page Count:
6
-
Published:2006
Citation
Diab, MO, Marque, C, & Khalil, M. "Modeling and Classification for Uterine EMG Signals Using Autoregressive Model." Intelligent Engineering Systems through Artificial Neural Networks, Volume 16. Ed. Dagli, CH, Buczak, AL, Enke, DL, Embrechts, M, & Ersoy, O. ASME Press, 2006.
Download citation file:
This article proposes a method for modeling and classification apply on the uterine contractions in the electromyogram (EMG) signal for the detection of preterm birth. The frequency content of the contraction changes from one woman to another and during pregnancy. Firstly we apply an AR model on the Uterine EMG signal for the calculation of the ai parameters. Wavelet decomposition is used to extract the parameters of each simulated contraction, and an unsupervised statistical classification method based on Fisher test is used to classify the signals. A principal component analysis projection is then used to evidence the groups resulting...
Abstract
I. Introduction
II. Autoregressive (AR) Model
III. Wavelet Transform
IV. Unsupervised Statistical Classification Method (USCM)
V. Results on Real Signals
VII. Discussion
VIII. Conclusion
References
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