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Heart rate variability dynamics for the prognosis of cardiovascular risk.

Ramirez-Villegas JF, Lam-Espinosa E, Ramirez-Moreno DF, Calvo-Echeverry PC, Agredo-Rodriguez W - PLoS ONE (2011)

Bottom Line: The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification.These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%).Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.

View Article: PubMed Central - PubMed

Affiliation: Computational Neuroscience, Department of Physics, Universidad Autonoma de Occidente, Cali, Colombia. juanfelipe.rv@gmail.com

ABSTRACT
Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.

Show MeSH
Box diagrams.(a) Statistical measures and, (b) Spectral measures from 5-min HRV records from normal (N) and risk (R) subjects in normalized values (y axis).
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pone-0017060-g003: Box diagrams.(a) Statistical measures and, (b) Spectral measures from 5-min HRV records from normal (N) and risk (R) subjects in normalized values (y axis).

Mentions: Statistical, spectral, multi-resolution and non-linear features were calculated from the recorded HRV database. The statistical analysis for the classical measures is shown in Figure 3.


Heart rate variability dynamics for the prognosis of cardiovascular risk.

Ramirez-Villegas JF, Lam-Espinosa E, Ramirez-Moreno DF, Calvo-Echeverry PC, Agredo-Rodriguez W - PLoS ONE (2011)

Box diagrams.(a) Statistical measures and, (b) Spectral measures from 5-min HRV records from normal (N) and risk (R) subjects in normalized values (y axis).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3046173&req=5

pone-0017060-g003: Box diagrams.(a) Statistical measures and, (b) Spectral measures from 5-min HRV records from normal (N) and risk (R) subjects in normalized values (y axis).
Mentions: Statistical, spectral, multi-resolution and non-linear features were calculated from the recorded HRV database. The statistical analysis for the classical measures is shown in Figure 3.

Bottom Line: The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification.These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%).Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.

View Article: PubMed Central - PubMed

Affiliation: Computational Neuroscience, Department of Physics, Universidad Autonoma de Occidente, Cali, Colombia. juanfelipe.rv@gmail.com

ABSTRACT
Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.

Show MeSH