Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure

Abstract

Abstract-A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropybased spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.

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

“…In our recent studies [20] and [21], the ratio between the powers in the modulation and respiratory frequency bands R provided an accuracy of 88.9% when classifying PB versus nPB. In this study, we have observed that the ratio R extracted from the original signal and the one extracted from its surrogate data present significant differences in nPB patients, but not in PB patients, thus reflecting a higher degree of nonlinearity in nPB patterns.…”

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.…”

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

“…Decreases in entropy of cardiac interbeat intervals have also been observed in conditions of cardiovascular stress associated with heart failure [16,30] at rest, and even in heart failure patients performing their usual daytime activities [6].…”

“…These results improved by applying a nonlinear spectral characterization to the respiratory flow signal through the innovative correntropy function [16]. In that database, the respiratory flow signal of heart failure patients at rest was acquired using a pneumotachograph.…”