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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.

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Box diagrams of Poincaré map-based features.(a) SD1 and SD2 ellipse fitting features and, (b) NN histogram (NN), width histogram (WNN) and length histogram (LNN) features of the 5-min HRV records from normal (N) and risk (R) subjects in normalized values (y axis).
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pone-0017060-g005: Box diagrams of Poincaré map-based features.(a) SD1 and SD2 ellipse fitting features and, (b) NN histogram (NN), width histogram (WNN) and length histogram (LNN) features of the 5-min HRV records from normal (N) and risk (R) subjects in normalized values (y axis).

Mentions: Non-linear analysis KS-test results are illustrated in Figure 5. The results of the non-linear analysis showed that SD1 and SD2 ellipse fitting features are statistically significant (, in the best case); SD1 being the less significant one. Additionally, there is statistical difference in histogram technique parameters, i.e., the widths of the NN intervals, the width and the length histograms of the HRV records, among normal and cardiovascular risk subjects. According to the statistical analysis, statistical significance increases with the NN intervals histogram width () and the length histogram width (). For the case of the width of the width histogram the statistical difference is relatively high ().


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 of Poincaré map-based features.(a) SD1 and SD2 ellipse fitting features and, (b) NN histogram (NN), width histogram (WNN) and length histogram (LNN) features of the 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-g005: Box diagrams of Poincaré map-based features.(a) SD1 and SD2 ellipse fitting features and, (b) NN histogram (NN), width histogram (WNN) and length histogram (LNN) features of the 5-min HRV records from normal (N) and risk (R) subjects in normalized values (y axis).
Mentions: Non-linear analysis KS-test results are illustrated in Figure 5. The results of the non-linear analysis showed that SD1 and SD2 ellipse fitting features are statistically significant (, in the best case); SD1 being the less significant one. Additionally, there is statistical difference in histogram technique parameters, i.e., the widths of the NN intervals, the width and the length histograms of the HRV records, among normal and cardiovascular risk subjects. According to the statistical analysis, statistical significance increases with the NN intervals histogram width () and the length histogram width (). For the case of the width of the width histogram the statistical difference is relatively high ().

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