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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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ECG signal in green line (record 108 containing several abnormal shapes, noise and artifacts).Left: MAP decision in red line based on M( = 3)-ary LRT. Right: A real time implementation of the matched filter-based method [15].
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pone-0110629-g006: ECG signal in green line (record 108 containing several abnormal shapes, noise and artifacts).Left: MAP decision in red line based on M( = 3)-ary LRT. Right: A real time implementation of the matched filter-based method [15].

Mentions: The experiments in this paper focus on the Arrhythmia Database which contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied between 1975 and 1979. The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range where several cardiologists independently annotated each record [20], altogether there are about 116137 QRS complexes. While some records contain clear R-peaks and few artifacts (e.g., records 100-107), for some records the detection of QRS complexes is very difficult due to abnormal shapes, noise and artifacts (e.g., records 108 and 207) as shown in figure 6. Note the different decision range for both detectors and the benefits of the proposed QRS decision (Fourier domain versus time domain).


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)

ECG signal in green line (record 108 containing several abnormal shapes, noise and artifacts).Left: MAP decision in red line based on M( = 3)-ary LRT. Right: A real time implementation of the matched filter-based method [15].
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110629-g006: ECG signal in green line (record 108 containing several abnormal shapes, noise and artifacts).Left: MAP decision in red line based on M( = 3)-ary LRT. Right: A real time implementation of the matched filter-based method [15].
Mentions: The experiments in this paper focus on the Arrhythmia Database which contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied between 1975 and 1979. The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range where several cardiologists independently annotated each record [20], altogether there are about 116137 QRS complexes. While some records contain clear R-peaks and few artifacts (e.g., records 100-107), for some records the detection of QRS complexes is very difficult due to abnormal shapes, noise and artifacts (e.g., records 108 and 207) as shown in figure 6. Note the different decision range for both detectors and the benefits of the proposed QRS decision (Fourier domain versus time domain).

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