Nonlinear Analysis Of Heart Rate Variability Research Articles

Conditions of autonomic reciprocal interplay versus autonomic co-activation: Effects on non-linear heart rate dynamics

The present study was aimed at investigating the autonomic nervous system influences on the fractal organization of human heart rate during sympathovagal interactions, with special emphasize on the short-term fractal organization in heart rate variability (HRV), as assessed by the scaling exponent ( α 1) of the detrended fluctuation analysis. Linear and non-linear HRV analyses were used to study the sympathetic and vagal modulation of heart rate in ten healthy men (mean ± SEM; age 26 ± 1 years) during conditions of 1) increased sympathetic activity and vagal withdrawal (head-up tilt), 2) decreased sympathetic activity and increased vagal outflow (thermoneutral upright head-out water immersion, WIn), and 3) simultaneous activation of the two arms of the autonomic nervous activity (upright head-out immersion in cold water, WIc). Hemodynamic and linear HRV results were consistent with previous reports during similar physiological conditions. α 1 increased significantly during head-up tilt (from 0.71 ± 0.13 supine to 0.90 ± 0.15 upright) and WIn (0.86 ± 0.10) and was significantly decreased during WIc (0.61 ± 0.15). Thus, α 1 increased when the cardiac autonomic interplay was altered in a reciprocal fashion, whatever the direction of the balance change. Conversely, α 1 decreased during the concomitant activation of both vagal and sympathetic activities. The results of linear analysis were necessary to precisely define the direction of change in autonomic control revealed by an increase in α 1, while the direction of change in α 1 indicated whether an increased vagal activity is coupled with a decreased or increased sympathetic activation. Using both linear and non-linear analysis of HRV may increase the understanding of changes in cardiac autonomic status.

Correlation between heart rate variability and haemodynamic fluctuation during induction of general anaesthesia: comparison between linear and non‐linear analysis*

The objective of this study was to compare linear and non-linear analysis of heart rate variability (HRV) in terms of correlation with haemodynamic fluctuation during induction of general anaesthesia. Pre-operatively, HRV was estimated by the MemCalc method in 114 patients scheduled for general anaesthesia. After anaesthesia was induced with propofol, fentanyl and vecuronium bromide, tracheal intubation was performed. Haemodynamic fluctuations during induction of anaesthesia were recorded and the correlation between pre-operative HRV and haemodynamic fluctuation was examined using logistic regression analysis. As an index of non-linear analysis of HRV, ultra short-term entropy (UsEn) correlated better with blood pressure fluctuation than did the ratio of the power of low frequency component of HRV to that of high frequency component (LF/HF). In contrast, although LF/HF significantly correlated with heart rate increase caused by tracheal intubation, the correlation between UsEn and heart rate fluctuation was not significant.

Nonlinear Heart Rate Variability Analysis May Predict Atrial Fibrillation After Coronary Artery Bypass Grafting

Heart rate variability might predict arrhythmias after coronary artery bypass grafting. Off-line processing of 10-min electrocardiogram recordings of consecutive patients provided R-R intervals for time domain, frequency domain, Poincaré, and point correlation analyses and subsequent association with postoperative atrial fibrillation by stepwise multivariate logistic regression. Of 88 patients who met entry criteria, 13 developed atrial fibrillation. Peak point correlation dimension (odds ratio 3.985/unit, P = 0.0096) and age (odds ratio 1.144/yr, P = 0.0019) were independently associated with atrial fibrillation (c-statistic = 0.839). Further study should confirm the ability of peak point correlation dimension to predict atrial fibrillation after coronary artery surgery with cardiopulmonary bypass.

Linear and nonlinear methods for analyses of cardiovascular variability in bipolar disorders

Heart rate variability (HRV), blood pressure variability (BPV), and the assessment of baroreflex sensitivity are widely accepted methods for analyzing and characterizing cardiovascular regulation and for an enhanced risk evaluation in different diseases. As a result of the complexity of the investigated regulatory systems, univariate analyses do not often provide a convenient description of pathological changes in the cardiovascular regulation. Therefore, the application of a multivariate approach is preferable. We present principal methods of time-domain, frequency-domain, and nonlinear analyses of HRV, BPV as well as methods for coupling and interaction analyses. Changes in autonomic nervous system (ANS) tone are known to accompany various mental disorders. Depressive patients frequently complain of symptoms of ANS dysfunction, such as dry mouth, diarrhea, and insomnia. These clinical observations propose the assumption of altered autonomic dysfunction in these patients. In contrast to these clinical assumptions, inconsistent results have been found in studies of HRV in depressive patients. This work therefore covers a brief review of the literature in respect to bipolar disorder and the rationale to study autonomic changes in such a psychiatric disease. Prospective studies of cardiovascular changes in mania and depression are needed to evaluate a psychopathological state in connection with cardiovascular changes and cardiac morbidity and mortality. These studies should consider BPV, coupling and interaction analyses, the application of nonlinear methods, and a multivariate approach in addition to the traditional analysis of HRV.

Orthostatic Decrease in Cardiac Chaos During the Head-up Tilt Test in Patients With Vasovagal Syncope

Autonomic dysfunction contributes to orthostatic intolerance in vasovagal syncope (VVS), but as it has not been identified by spectral analysis of heart rate variability (HRV) in previous studies, the present hypothesis was that nonlinear analysis of HRV would identify the orthostatic intolerance in VVS. Twenty-six patients with VVS and 14 matched controls were subjected to 80-degree head-up tilt test (positive: 13 patients; negative: 13 patients and 14 controls). There were no differences in the orthostatic changes in the indices of spectral analyses of HRV among the 3 groups. The Lyapunov exponent (LE) was calculated from 200 consecutive RR-intervals to investigate chaotic behavior, and cardiac chaos was defined as the incidence of the presence of a positive finite LE. Orthostatic decreases in cardiac chaos were observed in the VVS patients (both the positive and negative groups), although there was no orthostatic decrease in the control group (ANOVA: p = 0.008). The receiver-operator characteristic curve indicated that cardiac chaos during the tilt identified VVS regardless of the results of the tilt (p < 0.001, sensitivity: 85.7%, specificity: 96.2%). The decrease in cardiac chaos during the tilt test was specific to patients with VVS, even if their response to the test was negative.

Symbolic Analysis of Heart Rate Dynamics before the Spontaneous Onset of Paroxysmal Atrial Fibrillation

Background and Objectives:Heart rate variability (HRV) analysis, by conventional measures, for predicting the risk of atrial fibrillation (AF) has inherent shortcomings. Recently, nonlinear HRV analysis methods have been developed to reveal heart rate dynamics not evident from usual HRV measures. The purpose of this study was to test the hypothesisheart rate dynamics are altered before the spontaneous onset of paroxysmal AF (PAF) using algorithms derived from symbolic dynamics based on nonlinear system theory. Subjects and Methods: This study included 34 subjects (30 males, 59±9 years): 17 PAF patients and 17 gender and age-matched con- trols, who underwent 24-hour Holter ECG. The dynamics of one hour of normal sinus rhythm before the onset of AF were assessed using 4 different symbolization algorithms, and quantified by Shannon entropy (SE) and Renyi entropy (RE). Results:The SE, RE and Error-corrected SE (ECSE) were larger in the PAF than the con- trol patients when the raw R-R data was assessed (Algorithm I). However, when reconstructed time series, ex- pressed as the time difference between 2 successive R-R interval, were analyzed (Algorithm II, III and IV), the entropy of the PAF patients were consistently smaller than those of the controls. Of the 4 different symboliza- tion algorithms, algorithms II and III showed a significant difference in the SE, ECSE and RE between the PAF and the control patients (p<0.05). The difference in the RE at q=4 was most significant (p<0.001). Of the various word patterns seen during analysis using algorithms II and III, a marker of heart rate variation, the frequency of (1,1,1) pattern, was two-fold higher than in the control patients. The frequency of this pattern was 1.5-fold higher in the PAF patients when algorithm IV was used. These results suggest that a few specific word patterns are repetitive in a pathologic condition, such as PAF, and the heart rate dynamics were decreased in PAF. Conclusion:HRV analysis by symbolic dynamics may be a useful adjunctive tool for the detection of ab- normal heart rate dynamics as a pathophysiological mechanism of PAF. (Korean Circulation J 2004;34(10): 953-962)

Comparability of nonlinear measures of heart rate variability between long- and short-term electrocardiographic recordings

N nonlinear measurements of heart rate variability (HRV) may reveal subtle changes in heart rate dynamics and the data support their superiority over conventional methods of HRV in predicting adverse cardiac events.1,2 Recent data on postinfarction patients with depressed left ventricular function suggest that the reduced short-term scaling exponent (a1) is a more powerful predictor of mortality than the traditional measurements of HRV.1,2 The studies evaluating the value of these methods in risk assessment have usually been done with 24-hour electrocardiographic (ECG) recordings.1,2 It would be important to know if similar information could be obtained from short-term ECG recordings. The comparability of conventional HRV measures derived from longversus short-term ECG recordings is quite good.3,4 However, the data on the comparability of the newer nonlinear measures of HRV obtained from longversus shortterm ECG recordings are very limited. The present study compares results of nonlinear HRV analysis performed in longversus short-term ECG recordings in healthy subjects and in postinfarction patients and determines which nonlinear dynamic parameters obtained from short-term recordings reliably reflect information provided by long-term recordings. • • • The study population consisted of 30 healthy subjects and 30 postinfarction patients. The healthy subjects (age 33 6 16 years [mean 6 SD], 15 men and 15 women, body mass index 24 6 3 kg/m [mean 6 SD], 9 smokers) had normal 12-lead ECG findings, did not have any evidence of disease or other health problems, did not have symptom, and were not on medication. The clinical characteristics of postinfarction patients are shown in Table 1. ECG data were recorded using the Burdick Holter recorder (Spacelab-Burdick, Milton, Wisconsin) allowing for 20 minutes of high-resolution research electrocardiography (1,000 Hz) followed by 24 hours of continuous ECG recording (200 Hz). ECG recordings were performed during the hospital stay for the postinfarction patients. However, for 24-hour and 20minute ECG recordings, the resolution of the tachogram used for HRV analysis was 5 ms. During the 20-minute recordings, the study subjects were in supine resting position. ECG data were transferred to a microcomputer for processing and HRV analysis. Premature beats and noise were excluded automatically and manually. HRV was analyzed by a software package with methods that have been previously described.5–8 Because of differences in autonomic tone under resting and ambulatory conditions, the 24-hour HRV data were compared with 2 different types of 10-minute HRV data: (1) 10-minute data obtained from the beginning of the 20-minute resting state recordings, and (2) 10-minute data obtained from the beginning of the 24-hour ambulatory data. All the time-domain and nonlinear measures of HRV were analyzed in 600-second blocks and the average values were used for comparisons. SD of all normal-to-normal RR intervals (SDNN), the square root of the mean squared differences of successive normal-to-normal RR intervals (RMSSD), and the proportion of interval differences of successive normal-to-normal RR intervals .50 ms (pNN50) were calculated as standard time-domain measures of HRV. The ratio of the low-frequency (LF, 0.04 to 0.15 Hz) component to the high-frequency (HF, 0.15 to 0.40 Hz) component of spectral power (LF/HF ratio) obtained by using the fast Fourier transformation method was determined as a frequency-domain measure of HRV. The analysis was done in 512-beat blocks. In the present study, 2 nonlinear HRV parameters From the Cardiology Unit, Department of Medicine, University of Rochester School of Medicine and Dentistry, Rochester, New York; and the Division of Cardiology, Department of Medicine, University of Oulu, Oulu, Finland. This study was supported in part by grants from the Academy of Finland, the Paavo Nurmi Foundation and the Maritza and Reino Salonen Foundation, Helsinki, Finland. Dr. Perkiomaki’s address is: Cardiology Unit, Department of Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Box 653, Rochester, New York 14642. E-mail: heartjsp@heart.rochester.edu. Manuscript received September 14, 2000; revised manuscript received and accepted October 26, 2000. TABLE 1 Clinical Characteristics of the Postinfarction Patients

Linear and non-linear 24 h heart rate variability in chronic heart failure

It has recently been demonstrated that SDNN of heart rate variability (HRV) is a useful independent prognostic tool in chronic heart failure (CHF). The purpose of the present study was to evaluate if spectral and non-linear analysis of 24-h HRV, considered markers of autonomic cardiac modulation, contain independent prognostic information in CHF patients. Twenty normal subjects and thirty consecutive outpatients with clinically stable CHF were studied for 2 years. Periods of 300 R–R intervals were analyzed from Holter recordings. The power spectral analysis, the slope of the linear relationship between log-power versus log-frequency (1/ f), and the complexity content (using corrected conditional entropy; CCE) of the R–R series were calculated. The normalized power of the low frequency spectral component (LF) and the 1/ f slope were significantly lower in patients compared to controls (respectively 30.1±3.0 vs. 48.6±3.4 and −1.27±0.04 vs.−1.08±0.05; P<0.05). Moreover, the patients who died during the study presented a reduced LF (20.9±4.1 vs. 35.5±3.5 nu; P<0.05) and a steeper 1/ f slope (−1.40±0.09 vs. −1.21±0.04 nuts, P<0.05) compared to survivors. These results remained significant in a logistic model including heart rate and SDNN. The information content present in spectral and non-linear analysis of HRV in CHF patients has prognostic relevance independently from the time domain measures of HRV. In particular, the reduction of LF power seems the best indicator among those considered.