Predicting the transition to acute heart failure by refined multiscale entropy analysis of heart rate variability in chronic heart failure patients

2013 ◽  
Vol 46 (6) ◽  
pp. 622 ◽  
Author(s):  
N. Balogh ◽  
S. Khoor ◽  
T. Szuszai ◽  
I. Kecskes ◽  
P. Kecskemethy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Acute Heart Failure ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Heart Failure Patients

scholarly journals Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ping Cao ◽  
Bailu Ye ◽  
Linghui Yang ◽  
Fei Lu ◽  
Luping Fang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Characteristic Curve ◽  
Clinical Analysis ◽  
Healthy Controls ◽  
Heart Failure Patients ◽  
Rr Interval ◽  
Frequency Components

Objective. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed. Approach. We assess preprocessing (i.e., interpolation and resampling) of RR interval signals on the diagnostic effect of DC and AC from simulation and clinical data. The simulation analysis synthesizes unevenly sampled RR interval signals with known frequency components to evaluate the preprocessing performance for frequency extraction. The clinical analysis compares the conventional DC and AC calculation with the calculation using preprocessed RR interval signals on 24-hour data acquired from normal subjects and chronic heart failure patients. Main Results. The assessment of frequency components in the RR intervals using wavelet analysis becomes more robust with preprocessing. Moreover, preprocessing improves the diagnostic ability based on DC and AC for chronic heart failure patients, with area under the receiver operating characteristic curve increasing from 0.920 to 0.942 for DC and from 0.818 to 0.923 for AC. Significance. Both the simulation and clinical analyses demonstrate that interpolation and resampling of unevenly sampled RR interval signals improve the performance of DC and AC, enabling the discrimination of CHF patients from healthy controls.

Nonlinear Indices of Heart Rate Variability in Chronic Heart Failure Patients: Redundancy and Comparative Clinical Value

2007 ◽  
Vol 18 (4) ◽  
pp. 425-433 ◽  
Author(s):  
ROBERTO MAESTRI ◽  
GIAN DOMENICO PINNA ◽  
AGOSTINO ACCARDO ◽  
PAOLO ALLEGRINI ◽  
RITA BALOCCHI ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Clinical Value ◽  
Heart Failure Patients

scholarly journals Heart rate variability parameters among acute heart failure patients in a tertiary center in Nigeria

2020 ◽  
Vol 7 (2) ◽  
pp. 178-184
Author(s):  
Adeyemi OA ◽  
Akinlade OM ◽  
Ogunmodede JA ◽  
Kolo PM ◽  
Katibi IA ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Acute Heart Failure ◽  
Heart Failure Patients ◽  
Tertiary Center

Clinical correlates of non-linear indices of heart rate variability in chronic heart failure patients

2006 ◽  
Vol 51 (4) ◽  
pp. 220-223 ◽  
Author(s):  
Roberto Maestri ◽  
Gian Domenico Pinna ◽  
Rita Balocchi ◽  
Gianni D'Addio ◽  
Manuela Ferrario ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Heart Failure Patients ◽  
Clinical Correlates ◽  
Non Linear

Circadian pattern of heart rate variability in chronic heart failure patients Effects of physical training

1995 ◽  
Vol 16 (10) ◽  
pp. 1380-1386 ◽  
Author(s):  
S. ADAMOPOULOS ◽  
P. PONIKOWSKI ◽  
E. CERQUETANI ◽  
M. PIEPOLI ◽  
G. ROSANO ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Physical Training ◽  
Circadian Pattern ◽  
Heart Failure Patients

USING DIFFERENT ENTROPIES TO ANALYZE THE HEART RATE VARIABILITY OF CONGESTIVE HEART FAILURE PATIENTS

2011 ◽  
Vol 23 (04) ◽  
pp. 253-260 ◽  
Author(s):  
Ren-Guey Lee ◽  
Chun-Chieh Hsiao ◽  
Chieh-Yi Kao
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Analysis Of Variance ◽  
Human Body ◽  
Approximate Entropy ◽  
Young Group ◽  
Multiscale Entropy ◽  
Heart Failure Patients

The purpose of this paper is to show the influence of congestive heart failure (CHF) on heart by using different entropies to apply on the group of patients with CHF and normal group. Three different entropies are used: approximate entropy (ApEn), multiscale entropy (MSE), and base-scale entropy (BsEn). We use these three entropies to measure the complexity of the heart rate variability (HRV) and also use analysis of variance (ANOVA) to analyze the result of entropies to discuss the feasibility of recognizing CHF patients by utilizing entropies. With the analysis results of different entropies, the influence of CHF on heart has also been clearly demonstrated. The results on the approximate entropy show that the normal young group has a higher approximate entropy value while the CHF group has a lower value. This can be explained as a healthy, strong heart that can change its heart rate freely to adapt the change of the environment or the needs of the human body, therefore the HRV will be more complex. From the ANOVA results of approximate entropy, it can be observed that the F value is larger than 1, but is still small. In other words, the approximate entropy can be used to distinguish the three groups, the effect is, however, not good. It is hard to recognize a CHF patient by using approximate entropy.

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