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Modeling Quasi-Periodic Signals by a Non-Parametric Model: Application on Fetal ECG Extraction

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Abstract

— Quasi-periodic signals can be modeled by their second order statistics as Gaussian process. This work presents a non-parametric method to model such signals. ECG, as a quasi-periodic signal, can also be modeled by such method which can help to extract the fetal ECG from the maternal ECG signal, using a single source abdominal channel. The prior information on the signal shape, and on the maternal and fetal RR interval, helps to better estimate the parameters while applying the Bayesian principles. The values of the pa-rameters of the method, among which the R-peak instants, are accurately estimated using the Metropolis-Hastings algorithm. This estimation provides very precise values for the R-peaks, so that they can be located even between the existing time samples.
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Dates and versions

hal-01080120 , version 1 (04-11-2014)

Identifiers

  • HAL Id : hal-01080120 , version 1

Cite

Saman Noorzadeh, Mohammad Niknazar, Bertrand Rivet, Julie Fontecave-Jallon, Pierre-Yves Guméry, et al.. Modeling Quasi-Periodic Signals by a Non-Parametric Model: Application on Fetal ECG Extraction. EMBC 2014 - 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, Chicago, United States. ⟨hal-01080120⟩
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