Risk assessment of diabetes mellitus by chaotic globals to heart rate variability via six power spectra

Nenhuma Miniatura disponível

Data

2017-09-01

Orientador

Coorientador

Pós-graduação

Curso de graduação

Título da Revista

ISSN da Revista

Título de Volume

Editor

Tipo

Artigo

Direito de acesso

Acesso restrito

Resumo

Background: The priniciple objective here is to analyze cardiovascular dynamics in diabetic subjects by actions related to heart rate variability (HRV). The correlation of chaotic globals is vital to evaluate the probability of dynamical diseases. Methods: Forty-six adults were split equally. The autonomic evaluation consisted of recording HRV for 30 minutes in supine position without any additional stimuli. Chaotic globals are then able to statistically determine which series of interbeat intervals are diabetic and which are not. Two of these chaotic globals, spectral Entropy and spectral Detrended fluctuation analysis were derived from six alternative power spectra: Welch, Multi-Taper Method, Covariance, Burg, Yule-Walker and the Periodogram. We then compared results to observe which power spectra provided the greatest significance by three statistical tests: One-way analysis of variance (ANOVA1); Kruskal-Wallis technique and the multivariate technique, principal component analysis (PCA). Results: The Chaotic Forward Parameter One (CFP1) applying all three parameters is proven the most robust algorithm with Welch and MTM spectra enforced. This was proven following two tests for normality where ANOVA1 (p=0.09) and Kruskal-Wallis (p=0.03). Multivariate analysis revealed that two principal components represented 99.8% of total variance, a steep scree plot, with CFP1 the most influential parameter. Conclusion: Diabetes reduced the chaotic response.

Descrição

Idioma

Inglês

Como citar

Romanian Journal of Diabetes, Nutrition and Metabolic Diseases, v. 24, n. 3, p. 227-236, 2017.

Itens relacionados

Financiadores