Windowed multivariate autoregressive model improving classification of labor vs. pregnancy contractions. - Inserm - Institut national de la santé et de la recherche médicale Access content directly
Journal Articles Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Year : 2013

Windowed multivariate autoregressive model improving classification of labor vs. pregnancy contractions.

Abstract

Analyzing the propagation of uterine electrical activity is poised to become a powerful tool in labor detection and for the prediction of preterm labor. Several methods have been proposed to investigate the relationship between signals recorded externally from several sites on the pregnant uterus. A promising recent method is the multivariate autoregressive (MVAR) model. In this paper we proposed a windowed (time varying) version of the multivariate autoregressive model, called W-MVAR, to investigate the connectivity between signals while still respecting their non-stationary characteristics. The proposed method was tested on synthetic signals as well as applied to real signals. The comparison between the two methods on synthetic signals showed the superiority of W-MVAR to detect connectivity even if it is non-stationary. The application of W-MVAR on multichannel real uterine signals show that the proposed method is a good tool to distinguish non-labor and labor signals. These results are very promising and can very possibly have important clinical applications in labor detection and preterm labor prediction.
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Dates and versions

inserm-00881734 , version 1 (08-11-2013)

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Brynjar Karlsson, Mahmoud Hassan, Catherine Marque. Windowed multivariate autoregressive model improving classification of labor vs. pregnancy contractions.. Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013, 2013, pp.7444-7. ⟨10.1109/EMBC.2013.6611279⟩. ⟨inserm-00881734⟩
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