Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients Gorriz Sáez, Juan Manuel Ramírez Pérez De Inestrosa, Javier Olivares, Alberto Padilla De La Torre, Pablo Puntonet, Carlos G. Cantón, Manuel Laguna, Pablo Algorithms Arrhythmia Database and informatics methods Electrocardiography Matched filters Signal filtering Signal processing Speech signal processing This paper shows an adaptive statistical test for QRS detection of electrocardiography (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 motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches 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. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, 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. 2014-11-24T07:37:43Z 2014-11-24T07:37:43Z 2014 journal article Górriz, J.M.; et al. Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients. Plos One, 9(10): e110629 (2014). [http://hdl.handle.net/10481/33852] 1932-6203 http://hdl.handle.net/10481/33852 10.1371/journal.pone.0110629 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Public Library of Science (PLOS)