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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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Patient 101 of the MIT-BIH arrhythmia database [20] sampled with
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pone-0110629-g004: Patient 101 of the MIT-BIH arrhythmia database [20] sampled with

Mentions: Figure 4 shows two examples of the database [20] and the results of the proposed QRS detector with the same proportion between the length of the window and the length of the overlap ()25%). The selection of the size of the window influences the performance of any detector since it controls the amount of information processed in the test. Typically the QRS complex lasts for about ms, thus a suitable selection for the observation window is samples.


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)

Patient 101 of the MIT-BIH arrhythmia database [20] sampled with
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110629-g004: Patient 101 of the MIT-BIH arrhythmia database [20] sampled with
Mentions: Figure 4 shows two examples of the database [20] and the results of the proposed QRS detector with the same proportion between the length of the window and the length of the overlap ()25%). The selection of the size of the window influences the performance of any detector since it controls the amount of information processed in the test. Typically the QRS complex lasts for about ms, thus a suitable selection for the observation window is samples.

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