A simplified QRS decision stage based on the DFT coefficients

This paper shows an adaptive statistical test for QRS detection of ECG signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The previous algorithms based on maximum a posteriori (MAP) estimation result in high signal model complexity which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. A simplified model based on the independent Gaussian properties of the DFT coefficients is proposed. This model allows to define a simplified MAP probability function and to define an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. Moreover, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.