Prediction of Paroxysmal Atrial Fibrillation Using Nonlinear Analysis of the R-R Interval Dynamics Before the Spontaneous Onset of Atrial Fibrillation

Abstract

Background New methods based on nonlinear theory have been developed to give more insight into complex heart rate (HR) dynamics. This study was designed to test the hypothesis that altered HR dynamics, as analyzed with complexity and fractal measures, may precede the spontaneous onset of paroxysmal atrial fibrillation (PAF). Secondly, the difference in the temporal change of these measurements between the different types of atrial fibrillation (AF) was assessed. Methods and Results From 105 Holter tapes in which PAF was recorded, 44 PAF (≥5 min) episodes in 33 patients (22 men, 58±12 years), preceded by sinus rhythm for more than 1 h, were selected and submitted to timeand frequency-domain HR variability analyses, along with detrended fluctuation analysis, approximate entropy (ApEn) and sample entropy (SampEn). The 60 min before the onset of AF were divided into 6 10-min periods and studied using repeated measures ANOVA. PAF episodes were divided into 3 subgroups: an increased HF component and decreased L/H ratio (vagal type, n=20); increased L/H ratio and decreased HF component (sympathetic type, n=14); and non-related type (n=10). None of the time-or frequency-domain parameters showed any significant change before AF in any type of AF. The 1 showed a tendency to decrease before the onset of AF and the change in 1 was divergent according to the AF type. The ApEn decreased before the onset of AF (1.005±0.046, 60-50 min before AF to 0.894±0.052, 10-0 min before AF; p=0.032). The SampEn also decreased progressively before the start of AF (1.165± 0.085, 60-50 min before AF to 0.887±0.077, 10-0 min before AF, p=0.003). The decrease in both the ApEn and SampEn was irrespective of the AF type. Conclusions A reduction in the ApEn and SampEn, which reflects the nonlinear complexity of HR variability, is a hallmark of altered HR dynamics preceding the spontaneous onset of AF. (Circ J 2006; 70: 94 -99)

Entropy in Investigation of Vasovagal Syndrome in Passive Head Up Tilt Test

“…Changes in the entropies (ApEn, SampEn) were already reported in some cardiovascular researches [21][22][23][24], postural control investigations [25,26], physical activity measures [27,28] and in analysis of cardiodepressive vasovagal syncope [24]. The presented analysis of changes in the complexity of joint signals of ECG, ICG and blood pressure during passive provocation of the neuro-cardiogenic reaction will add to further understanding of the course and mechanism of vasovagal syndrome.…”

“…The in-depth description of the comparisons of both of these statistics can be found in the literature [20]. Despite elementary differences between ApEn and SampEn, both statistics are extensively used in cardiovascular physiology [20,22,23,[27][28][29][30][31][33][34][35][36][40][41][42]. In this paper we also show the possible applications of ApEn (m, r, N) and SampEn (m, r, N) for the analysis of physiological signals.…”

Long-Term Follow-up of the Circadian Rhythm of Heart Rate and Heart Rate Variability in Healthy Elderly Patients

“…15,16 HRV obtained from Holter monitoring as indices of ANA is known to be reproducible and reliable measure. 21,[23][24][25] Longitudinal studies are expected to show the features of age-related change more precisely and in more detail than cross-sectional studies, especially in a small population. Thus, to characterize the age-related changes of ANA in elderly patients, we analyzed the longitudinal age-related changes in HR, HRV, and their circadian rhythms over 15 years in 15 healthy elderly patients at a mean age of 70 years, and assessed the age-related changes in the dynamics of ANA in the morning.…”

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG

“…The main methods used in HRV analysis are time-domain, frequency domain, and non-linear [3][4][5][6][7]. The time-domain method proposes limitations on the fluctuation of heart rate.…”

“…Rudolf Clausius developed entropy, which has been used to signal the analysis area to characterize the complexity of a system or signal [16]. The analysis of fractal structure and complexity of HRV signals based on entropy [5,9,12] and other nonlinear methods have been extremely popular throughout the world. Wavelet transform methods lacked the analysis of the chaos of HRV signals.…”