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Real time QRS detection based on M-ary likelihood ratio test on the DFT coefficients.

Górriz JM, Ramírez J, Olivares A, Padilla P, Puntonet CG, Cantón M, Laguna P - PLoS ONE (2014)

Bottom Line: This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Signal Theory, Telematics and Communications, CITIC, University of Granada, Granada, Spain.

ABSTRACT
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.

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Hypothesis considered for the derivation of the approximate M-ary LRT and its expected value.Left: Example of ECG segment (blue line) and its observation window composed of QRS (red line) and noise (black line) frames (M = 5 and r = 1). Right: The most probable hypotheses in  and  for a transition as shown in left figure.
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pone-0110629-g002: Hypothesis considered for the derivation of the approximate M-ary LRT and its expected value.Left: Example of ECG segment (blue line) and its observation window composed of QRS (red line) and noise (black line) frames (M = 5 and r = 1). Right: The most probable hypotheses in and for a transition as shown in left figure.

Mentions: For a simplification of (13) a particular transition is analyzed (see figure 2). This corresponds to a situation in which (being V the number of noise samples) observations in the buffer of size are QRS frames from a total of QRS frames. The most probable hypotheses in and , denoted by and respectively, are evaluated by taking the max logarithms in (12):


Real time QRS detection based on M-ary likelihood ratio test on the DFT coefficients.

Górriz JM, Ramírez J, Olivares A, Padilla P, Puntonet CG, Cantón M, Laguna P - PLoS ONE (2014)

Hypothesis considered for the derivation of the approximate M-ary LRT and its expected value.Left: Example of ECG segment (blue line) and its observation window composed of QRS (red line) and noise (black line) frames (M = 5 and r = 1). Right: The most probable hypotheses in  and  for a transition as shown in left figure.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214710&req=5

pone-0110629-g002: Hypothesis considered for the derivation of the approximate M-ary LRT and its expected value.Left: Example of ECG segment (blue line) and its observation window composed of QRS (red line) and noise (black line) frames (M = 5 and r = 1). Right: The most probable hypotheses in and for a transition as shown in left figure.
Mentions: For a simplification of (13) a particular transition is analyzed (see figure 2). This corresponds to a situation in which (being V the number of noise samples) observations in the buffer of size are QRS frames from a total of QRS frames. The most probable hypotheses in and , denoted by and respectively, are evaluated by taking the max logarithms in (12):

Bottom Line: This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Signal Theory, Telematics and Communications, CITIC, University of Granada, Granada, Spain.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus