Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

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

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

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

Pairwise Measures of Causal Direction in the Epidemiology of Sleep Problems and Depression

Peer reviewe

Development of the scale of perceived social support in HIV (PSS-HIV)

Social support (SS) plays a key role for HIV/AIDS prevention and disease management. Numerous general and disease-specific SS instruments have been developed and perception of support has been increasingly considered, though no scales have been specifically developed to measure perceived social support (PSS) in HIV/AIDS. To help fill this gap a 12-item scale was developed. The study comprised 406 (HIV(+) and HIV(−)) participants from Chile and the UK. A principal component factor analysis yielded three factors explaining 77.0 % of the total variance: Belonging, Esteem and Self-development with Cronbach α of 0.759, 0.882 and 0.927 respectively and 0.893 on the full scale. The PSS-HIV is brief, easy-to-apply, available in English and Spanish and evaluates the perception of supportive social interactions. Further research is needed to corroborate its capacity to detect psycho–socio–immune interactions, its connection with Maslow’s hierarchy of need theory and to evaluate its properties for different health states