Correntropy-based nonlinearity test applied to patients with chronic heart failure

Abstract

Abstract-In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude

“…Therefore, periodic breathing seems to reduce complexity within the variability of ventilation and HR. The reduced complexity within the corresponding physiologic signals in heart failure patients with breathing disorders and higher risk condition is consistent with the notion that entropy measures are negatively correlated with conditions of physiologic stress such as hypoxia or heart failure [17].…”

Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing

“…Also, the multivariate logistic regression model developed using features extracted from CSD-based analysis provided better classification results, in terms of accuracy, sensitivity, specificity and AUC, than the model created with PSD-based features. The reason for this improvement could be the capability of the correntropy, and subsequently CSD, to preserve nonlinear characteristics and high-order moments of the data [16,28,29], in this case PPIs, which have been previously documented as chaotic or nonlinear [11,12]. In addition, we justified the use of CSD by demonstrating that the majority of the patients showed evidence of nonlinear PPIs behaviour.…”