Heart rate variability, multifractal multiscale patterns and their assessment criteria

Abstract

Objective: Both the central nervous system and the autonomic nervous system are complex physiological networks which modulate the heart rate. They are spatially extended, have built-in delays and work on many time scales simultaneously—nonhomogeneous networks with multifractal dynamics. The object of our research was the analysis of human heart rate variability (HRV) using the nonlinear multiscale multifractal analysis (MMA) method for several cardiovascular diseases. The analysis of HRV (night-time recordings) involved six groups of patients: 61 healthy persons, 104 cases with aortic valve stenosis, 42 with hypertrophic cardiomyopathy, 36 with atrial fibrillation, 70 patients with coronary artery disease and 19 with congestive heart failure. 85% of patients formed a training data set (282 subjects) and 15% formed a test data set (50 subjects). Approach: Multiscale multifractal analysis allows one to analyze the complexity of HRV and find the scaling properties of its fluctuations. The main result of MMA is the Hurst surface, the shape of which changes depending on the medical case analyzed. We prepared six criteria to distinguish a multifractal pattern for healthy subjects. We also prepared additional criteria, enabling one to recognize atrial fibrillation. Main results: For the training data set, we obtained the following accuracy statistics in distinguishing the patients from the healthy: 68% for coronary artery disease, 67% for hypertrophic cardiomyopathy, 88% for atrial fibrillation, 74% for aortic valve stenosis and 83% for congestive heart failure. For the complete training data set we obtained an accuracy of 73%, and 80% for the test data set (mean for ten random selections of the test data set). Significance: The results of MMA presented here provide an additional input into the diagnostic process and may help to create a paradigm for future studies on medical screening methods, especially in that MMA focuses on very low frequencies of HRV not easily accessible by standard medical techniques. Satisfactory statistics for screening using both MMA and the unfiltered version of LF/HF indicate that the nature of the complete network moderating heart rhythm needs to be studied and that sinus rhythm in clinical patients may not always be separated from arrhythmia when its incidence is large.

The effect of persistent U-shaped patterns in RR night-time series on the heart rate variability complexity in healthy humans

“…The shape depicted for the original data (part a) has a characteristic 'ridge' along the range of q parameter between 0 and 2 and for all scales s (although the 'ridge' changes direction for lower scales). Such a ridge is observed usually for healthy subjects (this 'ridge' is marked in figure 5(a) by a red ellipse) (Gierałtowski et al 2012, Kokosińska et al 2018. Figure 5(b) depicts the shape of the Hurst surface obtained from the same RR time series, but with the U-shaped patterns replaced by 1/f noise.…”

Asymmetric multiscale multifractal analysis (AMMA) of heart rate variability

“…The aim of this paper is to apply the new AMMA technique to the RR intervals time series in order to see if this asymmetric technique improves the statistical performance on the MMA-based screening method (Kokosińska et al 2018) and also reveals the asymmetry inherent in heart rate.…”

“…The motivation to apply the algorithm to periods of a decrease and of an increase of the interbeat intervals using the asymmetric version of the MMA method (AMMA) is thus to enhance the possibility to interpret the results obtained during MMA analysis. In (Kokosińska et al 2018), we showed that MMA analysis allows us to distinguish various groups of patients with different diseases. On the other hand, HRA analysis has been shown to be a promising way to distinguish different pathological conditions.…”

Inflammation and Reduced Parasympathetic Cardiac Modulation in Aortic-Valve Sclerosis

“…Among different mechanism affected or modified by aortic-valve disease, the autonomic regulation, as assessed by heart rate variability (HRV), has been mainly examined in patients manifesting severe aortic-valve stenosis [7][8][9][10][11]. HRV refers to the interval fluctuations between consecutive heart beats [12,13].…”

“…These fluctuations in the heart rate are modulated by many mechanisms including the interaction with the respiratory cycle (respiratory sinus arrhythmia) [14], the baroreflex mechanism [15], and the autonomic nervous system modulation of the sinus node through norepinephrine released from sympathetic nerves and acetylcholine released from the parasympathetic nerves [16]. Such studies of severe aortic-valve stenosis evaluated HRV with statistical indices (such as the standard deviation of all heart-beats intervals) and provided evidence of an overall HRV reduction that indicates an impaired heart's autonomic control, presumably involving a vagal withdrawal and an increased sympathetic activity [7][8][9][10][11]. Other HRV indices based on power spectrum analysis estimate the contribution of the sympathetic and parasympathetic modulation on the HRV fluctuations at different frequencies [12,16].…”