Nonlinear Heart Rate Dynamics Research Articles

Chronic effect of resistant physical exercise on linear and nonlinear dynamics of heart rate variability in patients with type 2 diabetes mellitus

Introduction: Diabetes Mellitus (DM) can promote changes in the autonomic nervous system. Training with resistance exercise can contribute to the decrease of the glycemic level and improve the neural control of the heart. Objective: to verify the effects of resistance exercise training (RT) on linear and non-linear dynamics of autonomic heart rate (HR) modulation in patients with DM2. Methods: Eight patients with DM2 were studied. The R-R intervals of the HR were recorded in the supine resting condition and HRV was analyzed by linear and non-linear method. RT was performed for 12 weeks, with 60% 1RM, 5 exercises for large muscle groups. Results: data variations between post-RT and initial ones did not show statistical difference (p>0.05) in HRV calculated both by linear and non-linear methods. Conclusion: 12 weeks of moderate RT does not promote positive effects on HRV in patients with T2DM.

Water drinking during aerobic exercise improves the recovery of non-linear heart rate dynamics in coronary artery disease: crossover clinical trial.

The post-exercise recovery is a period of vulnerability of the cardiovascular system in which autonomic nervous system plays a key role in cardiovascular deceleration. It is already known that individuals with coronary artery disease (CAD) are at greater risk due to delayed vagal reactivation in this period. Water ingestion has been studied as a strategy to improve autonomic recovery and mitigate the risks during recovery. However, the results are preliminary and need further confirmation. Therefore, our aim was to investigate the influence of individualized water drinking on the non-linear dynamics of heart rate during and after aerobic exercise in CAD subjects. 30 males with CAD were submitted to a control protocol composed of initial rest, warming up, treadmill exercise, and passive recovery (60 min). After 48 hours they performed the hydration protocol, composed of the same activities, however, with individualized water drinking proportional to the body mass lost in the control protocol. The non-linear dynamics of heart rate were assessed by indices of heart rate variability extracted from the recurrence plot, detrended fluctuation analysis, and symbolic analysis. During exercise, the responses were physiological and similar in both protocols, indicating high sympathetic activity and reduced complexity. During recovery, the responses were also physiological, indicating the rise of parasympathetic activity and the return to a more complex state. However, during hydration protocol, the return to a more complex physiologic state occurred sooner and non-linear HRV indices returned to resting values between the 5th and 20th minutes of recovery. In contrast, during the control protocol, only a few indices returned to resting values within 60 minutes. Despite that, differences between protocols were not found. We conclude that the water drinking strategy accelerated the recovery of non-linear dynamics of heart rate in CAD subjects but did not influence responses during exercise. This is the first study to characterize the non-linear responses during and after exercise in CAD subjects.

PO-05-133 PROLONGED NEUROMODULATION EFFECTS AFTER PULMONARY VEIN ISOLATION IN PATIENTS WITH PAROXYSMAL ATRIAL FIBRILLATION: INSIGHT FROM THE NONLINEAR HEART RATE DYNAMICS

Heart rate variability (HRV) reveals information undetected by direct electrocardiogram morphology evaluation. Prior research indicated that patients with paroxysmal atrial fibrillation (PAF) had altered a short-term linear HRV after pulmonary vein isolation (PVI). However, the long-term changes in the nonlinear heart rate dynamics before and after PVI remain unclear. The objective was to investigate the long-term nonlinear HRV changes before and after PVI in PAF patients. We enrolled 42 drug-refractory symptomatic PAF patients who underwent PVI. Holter monitoring were done less than 1 year before, 1∼3, and 6∼12 months after PVI, respectively. The nonlinear HRV variables, including sample entropy (SampEn), multi-scale entropy (MSE), detrended fluctuation analysis (DFA), and Poincaré Plot analysis, were analyzed. Significant reductions in SD1, SD1/SD2, MSE 1∼5, MSE 6∼20, and SampEn occurred 6∼12 months after when compared to those before PVI, respectively. SD2, MSE slope 5, DFA slope 1 decreased 1∼3 months after PVI when compared to those before PVI, respectively, whereas increased significantly 6∼12 months after PVI when compared to those 1∼3 months after PVI, respectively. DFA slope2 increased significantly after PVI when compared to that before PVI. Detailed parameters are shown in the Table 1. As measured by DFA slope2, fractal correlation properties increased significantly following PVI. Heart rate complexity (MSE 1∼5, MSE 6∼20, and SampEn) and unpredictability of an RR time series (SD1/SD2) decreased significantly after PVI, whereas long-term RR interval (SD2) and short-term complexity (MSE slope 5) decreased in the 1∼3 months after PVI and then increased in 6∼12 months after PVI. These results indicated that the neuromodulation effects from PVI was far more prolonged and extensively than expected.Tabled 1Table 1. Serial changes in the HRV parametersPatients with PAF who underwent PVI (n = 42)< 1 year pre-PVI1∼3 months post-PVI6∼12 months post-PVISD121.16 ± 8.416.31 ± 11.08∗14.19 ± 8.63∗SD2177.58 ± 44.38134.92 ± 37∗146.46 ± 37.52∗+SD1/SD20.12 ± 0.060.12 ± 0.090.1 ± 0.06∗+DFA slope11.11 ± 0.240.8 ± 0.31∗0.94 ± 0.31∗+DFA slope21.07 ± 0.11.16 ± 0.13∗1.15 ± 0.11∗MSE 1∼55.62 ± 1.824.76 ± 1.74∗4.72 ± 1.59∗MSE 6∼2019.81 ± 5.6716.01 ± 5.6∗17.3 ± 4.75∗MSE slope50.06 ± 0.07-0.01 ± 0.09∗0.03 ± 0.07+SampEn1 ± 0.321.01 ± 0.290.9 ± 0.26∗+Data are expressed as (mean ± SD); DFA slope1: Detrended fluctuation analysis slope 1; DFA slope2: Detrended fluctuation analysis slope 2; MSE 1∼5, the summation of entropy values of scales 1 to 5; MSE 6∼20, the summation of entropy values of scales 6 to 20; MSE slope 5, the linear-fitted slope of scales 1 to 5; SD1, Poincaré plot standard deviation perpendicular the line of identity; SD2, Poincaré plot standard deviation along the line of identity; SD1/SD2, SD1-to-SD2 ratio; SampEn, sample entropy; *: p < 0.05 versus < 1 year pre-PVI; +: p < 0.05 versus 1∼3 months post-PVI Open table in a new tab

Nonlinear Heart Rate Dynamics Before and After Paroxysmal Atrial Fibrillation Events.

Heart rate complexity, derived from nonlinear heart rate variability (HRV), has been shown to help predict the outcomes of various diseases. Changes in heart rate complexity before and after paroxysmal atrial fibrillation (PAF) events are unclear. To evaluate changes in heart rate complexity through nonlinear HRV before and after PAF events. We enrolled 65 patients (72 ± 12.34 years old, 31 females) with 99 PAF events who received 24-hour Holter recording, and analyzed nonlinear HRV variables including Poincaré plot analysis, sample entropy (SampEn), and multiscale entropy (MSE). HRV analyses were applied to a 20-minute window before the onset and after the termination of PAF events. HRV parameters were evaluated and compared based on eight different 5-minute time segments, as we divided each 20-minute window into four segments of 5 minutes each. SampEn and MSE1~5 significantly decreased before the onset of PAF events, whereas SampEn, MSE1~5 and MSE6~20 significantly increased after the termination of PAF events. SD1 and SD2, which are nonlinear HRV parameters calculated via Poincaré plot analysis, did not significantly change before the PAF events, however they both decreased significantly after termination. Heart rate complexity significantly decreased before the initiation and increased after the termination of PAF events, which indicates the crucial role of nonlinear heart rate dynamics in the initiation and termination of PAF.

Enhanced detection of abnormalities in heart rate variability and dynamics by 7-day continuous ECG monitoring.

BackgroundThe analysis of heart rate variability (HRV) and heart rate (HR) dynamics by Holter ECG has been standardized to 24 hs, but longer‐term continuous ECG monitoring has become available in clinical practice. We investigated the effects of long‐term ECG on the assessment of HRV and HR dynamics.MethodsIntraweek variations in HRV and HR dynamics were analyzed in 107 outpatients with sinus rhythm. ECG was recorded continuously for 7 days with a flexible, codeless, waterproof sensor attached on the upper chest wall. Data were divided into seven 24‐h segments, and standard time‐ and frequency‐domain HRV and nonlinear HR dynamics indices were computed for each segment.ResultsThe intraweek coefficients of variance of HRV and HR dynamics indices ranged from 2.9% to 26.0% and were smaller for frequency‐domain than for time‐domain indices, and for indices reflecting slower HR fluctuations than faster fluctuations. The indices with large variance often showed transient abnormalities from day to day over 7 days, reducing the positive predictive accuracy of the 24‐h ECG for detecting persistent abnormalities over 7 days. Conversely, 7‐day ECG provided 2.3‐ to 6.5‐fold increase in sensitivity to detect persistent plus transient abnormalities compared with 24‐h ECG. It detected an average of 1.74 to 2.91 times as many abnormal indices as 24‐h ECG.ConclusionsLong‐term ECG monitoring increases the accuracy and sensitivity of detecting persistent and transient abnormalities in HRV and HR dynamics and allows discrimination between the two types of abnormalities. Whether this discrimination improves risk stratification deserves further studies.

Correlation Properties of Heart Rate Variability during a Marathon Race in Recreational Runners: Potential Biomarker of Complex Regulation during Endurance Exercise.

There is only very limited data examining cardiovascular responses in real-world endurance training/competition. The present study examines the influence of a marathon race on non-linear dynamics of heart rate (HR) variability (HRV). Eleven male recreational runners performed a self-paced marathon road race on an almost flat profile. During the race, heart rate and beat-to-beat (RR) intervals were recorded continuously. Besides HRV time-domain measurements, fractal correlation properties using short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) were calculated. The mean finishing time was 3:10:22 ± 0:17:56 h:min:s with a blood lactate concentration of 4.04 ± 1.12 mmol/L at the end of the race. Comparing the beginning to the end segment of the marathon race (Begin vs. End) significant increases could be found for km split time (p < .001, d = .934) and for HR (p = .010, d = .804). Significant decreases could be found for meanRR (p = .013, d = .798) and DFA-alpha1 (p = .003, d = 1.132). DFA-alpha1 showed an appropriate dynamic range throughout the race consisting of both uncorrelated and anti-correlated values. Lactate was consistent with sustained high intensity exercise when measured at the end of the event. Despite the runners slowing after halfway, DFA-alpha1 continued to fall to values seen in the highest intensity domain during incremental exercise testing in agreement with lactate assessment. Therefore, the discrepancy between the reduced running pace with that of the decline of DFA-alpha1, demonstrate the benefit of using this dimensionless HRV index as a biomarker of internal load during exercise over the course of a marathon race.

Heart rate dynamics and lactate following high-intensity race-pace continuous vs interval workouts in highly trained athletes.

The present study aimed to compare the physiological responses of high-intensity race-pace continuous vs. interval workouts commonly used in middle-distance athletics, by means of analyzing post-exercise cardiac autonomic regulation and lactate. Nineteen highly-trained 800-m male runners were asked to run a 600-m race-pace continuous workout and a 2 × 4 × 200-m interval training, counterbalanced and randomized within one week of difference. Blood lactate jointly with linear and nonlinear heart rate dynamics were assessed during the immediate 15-min recovery. Age-category (Under23-Senior vs. Juvenile-Junior) was considered as an inter-subject factor. Peak lactate was higher following the interval training (15.51 ± 0.99 vs 13.83 ± 1.77 mmol L-1; P < 0.05) whereas lactate removal was almost nonexistent 15 min after both workouts (between 0 and 16%). Vagal modulation (ln RMSSD and lnRMSSD to RR ratio) remained significantly depressed at the end of recovery following both workouts, although the alteration was larger following the interval training. Detrended Fluctuation Analysis evidenced a more random HR behavior (DFA1 closer to 0.5) during the first 9 min of recovery after the interval training, whereas no significant change was observed in heart rate complexity (SampEn). Neither were differences found in post-exercise lactate and HR dynamics as a function of age-category. High-intensity workouts commonly used in middle-distance athletics, both race-pace continuous and intervallic approaches, induce a large depression of vagal modulation in highly trained runners, although interval trainings appear to induce even a greater alteration of both linear and nonlinear HR dynamics and a higher post-exercise peak lactate.

Methods of Mathematical Analysis of Heart Rate Variability

This article reviews contemporary methods for analysis of heart rate variability. Along with classical processing methods using spectral and statistical assessments, novel approaches based on analysis of the nonlinear heart rate dynamics are also discussed.

Impact of Oxidative Stress on Long-Term Heart Rate Variability: Linear Versus Non-Linear Heart Rate Dynamics

Heart rate variability (HRV) is a widely used non-invasive and quantitative marker of cardiac autonomic control. Elevated oxidative stress (OS) and reduced HRV have been proven in specific disease subsets. However, the impact of OS on the long-term heart rate dynamics of both conventional linear and non-linear origin in the general population is not known. The 24-hour ambulatory electrocardiogram recordings and plasma 8-iso-prostaglandin F2α (8-iso-PGF2α) levels as an OS marker were acquired simultaneously in 71 consecutive patients. The conventional time and frequency domain HRV parameters and non-linear parameters were measured. The 8-iso-PGF2α is a significant determinant of most long-term conventional time and frequency domain HRV parameters and standard deviation (SD1, perpendicular to the line of identity; SD2, along the line of identity) descriptors from Poincaré plot analysis, but not of non-linear complexity and fractal parameters. Patients with a high OS burden had lower absolute low-frequency and high-frequency powers during both the night and morning periods, with a significant decrease in high-frequency power in the morning. Oxidative stress is one of the significant determinants of the HRV. The severity of OS is reflected in the conventional time and frequency domain HRV parameters, but not in the non-linear measurements.

The Effect of Breathing at the Resonant Frequency on the Nonlinear Dynamics of Heart Rate

Investigation of the nonlinear dynamics of the heart rate is crucial for understanding the physiological mechanisms of various functional states and understanding the adaptation capacity of the human body. The purpose of this study was to evaluate some quantitative parameters of the nonlinear parameters of resonance in the heart rate variability. The study included 70 healthy students who underwent paced breath training. The heart rate was recorded during breathing at different respiratory rates (spontaneous breathing, 4.5, 5, 5.5, 6, and 6.5 breaths/min). The resonant frequency was determined using heart-rate spectral analysis. Nonlinear analysis of HRV was performed using entropy measures (ApEn, SampEn, and MSE), Correlation dimension, Detrended Fluctuation Analysis (DFA), Recurrence Plot (REC, DET, Lmean, Lmax, and ShanEn), the Poincare plot (SD1 and SD2), and time asymmetry (GI and P). We observed a decrease in the level of complexity, an increase in the self-similarity of the RR intervals, a decrease in the chaotic component, and an increase in rhythm asymmetry during breathing at resonant frequency.

Effect of Yoga on Nonlinear Dynamics of Heart Rate in Prediabetic Subjects

Effect of Yoga on Nonlinear Dynamics of Heart Rate in Prediabetic Subjects

The effect of paced breathing at the resonance frequency on nonlinear features of heart rate variability

IntroductionHeart rate variability (HRV) biofeedback with paced breathing at the resonance frequency is a method for increasing vagal heart rate (HR) regulation and psychological functioning. However, the nature and mechanism underlying this effect are not well understood yet. The investigation of nonlinear dynamics of HR is a useful research and diagnostic instrument for understanding intrinsic mechanisms of resonances in the cardiovascular system, caused by paced breathing. The aim of this study was to test how the paced respiration at the resonance frequency affects the nonlinearities in HRV.MethodsThe study group consisted of 70 healthy students, who underwent paced breath training. HR was recorded in volunteers, breathing at different respiratory rates (spontaneous breathing, 4.5, 5, 5.5, 6, 6.5 breaths/min). The resonant frequency was determined using HR spectral analysis. Short‐term 5‐min HRV indices from the linear time domain (SDNN) and from nonlinear methods ((ApEn, SampEn, MSE), Correlation dimension D2, Detrended Fluctuation Analysis (DFA), Recurrence Plot (REC, DET, Lmean, Lmax, ShanEn), Poincaré plot (SD1, SD2), time asymmetry (GI, P)) were computed. Statistical analysis was performed using the Sign test.Results and discussionSDNN was significantly lower during free breathing (42.08 ± 2.25 ms) than during resonance (79.64 ± 3.4; p &lt; 0.001). Correlation dimension analysis of HRV showed that the D2 does not differ in control compared with resonance breathing (p &gt; 0.05). SD1 and SD2 Poincaré parameters during spontaneous breathing were significantly smaller (respectively, 28.91 ± 1.67 ms and 51.51 ± 2.83 ms) than those during resonance breathing (respectively, 35.61 ± 1.68 ms and 106.79 ± 4.55 ms; p &lt; 0.001). SampEn and ApEn were significantly higher during free breathing (respectively, 1.65 ± 0.03 and 1.22 ± 0.02) than in resonance breathing (1.02 ± 0.03; p &lt; 0.001). DFA alpha‐1 was smaller during free breathing (1.03 ± 0.03) than that during paced breathing (1.55 ± 0.02; p &lt; 0.001). In relation to long‐term exponents (DFA alpha‐2), there was a significant difference between free breathing (0.31 ± 0.01) and resonance breathing (0.14 ± 0.01; p &lt; 0.01). Resonance breathing induces significant increases in time irreversibility indices (GI: from 50.88 ± 0.61 to 57.38 ± 0.67, p &lt; 0.001; P: from 49.01 ± 0.44 to 54.92 ± 0.54, p &lt; 0.001). Recurrence plot analysis reveals a decrease in complexity during resonance breathing: REC increases from 24.77 ± 0.82 to 30.38 ± 0.27 (p &lt; 0.01), DET increases from 96.79 ± 0.18 to 98.81 ± 0.06 (p &lt; 0.01), ShanEn increases from 2.97 ± 0.03 to 3.35 ± 0.02 (p &lt; 0.01). The higher level of Lmean during resonance breathing (17.03 ± 0.51 vs 9.33 ± 0.29; p &lt; 0.01) indicates the transition from chaotic to periodic RR sequences. The complexity of RR signals, as measured by the MSE method, was significantly lower during resonance breathing than during free respiration.ConclusionsResonance breathing is characterized by simultaneous increasing of HRV and time asymmetry and decreasing complexity. Cardiovascular resonance is accompanied by a transition of the HR regulation system from chaos to order. These results are inconsistent with previous studies, showing that decreased nonlinear complexity is a dynamical signature of disrupted physiologic control systems.Support or Funding InformationThis work was supported by the Ministry of Education (№19.9737.2017/BCh).This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Novel Postoperative Atrial Fibrillation Risk Prediction from Preoperative High-Resolution ECG-Based Assessment of Nonlinear Heart Rate Dynamics

Novel Postoperative Atrial Fibrillation Risk Prediction from Preoperative High-Resolution ECG-Based Assessment of Nonlinear Heart Rate Dynamics

Relationship between linear and nonlinear dynamics of heart rate and impairment of lung function in COPD patients.

BackgroundIn chronic obstructive pulmonary disease (COPD), functional and structural impairment of lung function can negatively impact heart rate variability (HRV); however, it is unknown if static lung volumes and lung diffusion capacity negatively impacts HRV responses. We investigated whether impairment of static lung volumes and lung diffusion capacity could be related to HRV indices in patients with moderate to severe COPD.MethodsSixteen sedentary males with COPD were enrolled in this study. Resting blood gases, static lung volumes, and lung diffusion capacity for carbon monoxide (DLCO) were measured. The RR interval (RRi) was registered in the supine, standing, and seated positions (10 minutes each) and during 4 minutes of a respiratory sinus arrhythmia maneuver (M-RSA). Delta changes (Δsupine-standing and Δsupine-M-RSA) of the standard deviation of normal RRi, low frequency (LF, normalized units [nu]) and high frequency (HF [nu]), SD1, SD2, alpha1, alpha2, and approximate entropy (ApEn) indices were calculated.ResultsHF, LF, SD1, SD2, and alpha1 deltas significantly correlated with forced expiratory volume in 1 second, DLCO, airway resistance, residual volume, inspiratory capacity/total lung capacity ratio, and residual volume/total lung capacity ratio. Significant and moderate associations were also observed between LF/HF ratio versus total gas volume (%), r=0.53; LF/HF ratio versus residual volume, %, r=0.52; and HF versus total gas volume (%), r=−0.53 (P<0.05). Linear regression analysis revealed that ΔRRi supine-M-RSA was independently related to DLCO (r=−0.77, r2=0.43, P<0.05).ConclusionResponses of HRV indices were more prominent during M-RSA in moderate to severe COPD. Moreover, greater lung function impairment was related to poorer heart rate dynamics. Finally, impaired lung diffusion capacity was related to an altered parasympathetic response in these patients.

Poor glycemic control impacts linear and non-linear dynamics of heart rate in DM type 2

INTRODUCTION: It is well known that type 2 diabetes mellitus (T2DM) produces cardiovascular autonomic neuropathy (CAN), which may affect the cardiac autonomic modulation. However, it is unclear whether the lack of glycemic control in T2DM without CAN could impact negatively on cardiac autonomic modulation. Objective: To evaluate the relationship between glycemic control and cardiac autonomic modulation in individuals with T2DM without CAN. Descriptive, prospective and cross sectional study.METHODS: Forty-nine patients with T2DM (51±7 years) were divided into two groups according to glycosylated hemoglobin (HbA1c): G1≤7% and G2&gt;7.0%. Resting heart rate (HR) and RR interval (RRi) were obtained and calculated by linear (Mean iRR; Mean HR; rMSSD; STD RR; LF; HF; LF/HF, TINN and RR Tri,) and non-linear (SD1; SD2; DFα1; DFα2, Shannon entropy; ApEn; SampEn and CD) methods of heart rate variability (HRV). Insulin, HOMA-IR, fasting glucose and HbA1c were obtained by blood tests.RESULTS: G2 (HbA1c≤7%) showed lower values for the mean of iRR; STD RR; RR Tri, TINN, SD2, CD and higher mean HR when compared with G1 (HbA1c &gt; 7%). Additionally, HbA1c correlated negatively with mean RRi (r=0.28, p=0.044); STD RR (r=0.33, p=0.017); RR Tri (r=-0.35, p=0.013), SD2 (r=-0.39, p=0.004) and positively with mean HR (r=0.28, p=0.045). Finally, fasting glucose correlated negatively with STD RR (r=-0.36, p=0.010); RR Tri (r=-0.36, p=0.010); TINN (r=-0.33, p=0.019) and SD2 (r=-0.42, p=0.002).CONCLUSION: We concluded that poor glycemic control is related to cardiac autonomic modulation indices in individuals with T2DM even if they do not present cardiovascular autonomic neuropathy.

Recovery of linear and nonlinear heart rate dynamics after coronary artery bypass grafting surgery

Conventional coronary artery bypass grafting (C-CABG) and off-pump CABG (OPCAB) surgery may produce different patients' outcomes, including the extent of cardiac autonomic (CA) imbalance. The beneficial effects of an exercise-based inpatient programme on heart rate variability (HRV) for C-CABG patients have already been demonstrated by our group. However, there are no studies about the impact of a cardiac rehabilitation (CR) on HRV behaviour after OPCAB. The aim of this study is to compare the influence of both operative techniques on HRV pattern following CR in the postoperative (PO) period. Cardiac autonomic function was evaluated by HRV indices pre- and post-CR in patients undergoing C-CABG (n = 15) and OPCAB (n = 13). All patients participated in a short-term (approximately 5 days) supervised CR programme of early mobilization, consisting of progressive exercises, from active-assistive movements at PO day 1 to climbing flights of stairs at PO day 5. Both groups demonstrated a reduction in HRV following surgery. The CR programme promoted improvements in HRV indices at discharge for both groups. The OPCAB group presented with higher HRV values at discharge, compared to the C-CABG group, indicating a better recovery of CA function. Our data suggest that patients submitted to OPCAB and an inpatient CR programme present with greater improvement in CA function compared to C-CABG.

Altered electrophysiological properties and deranged cardiac autonomic modulation predispose patients to atrial fibrillation after arrested heart operations

Background Advanced Heart Rate (HR) Variability analyses applying non-linear dynamics and chaos theory provide information about derangement of cardiac autonomic modulation predicting ventricular or atrial arrhythmias. Aim of the present study was to determine which high-resolution ECG and non-linear HR dynamics parameters predispose to development of postoperative atrial fibrillation after surgery on the open arrested heart.

Effect of Calculation Algorithm on Heart Rate Variability by Chaos Theory

Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-tobeat interval. HRV parameters providing information about the scaling behavior or the complexity of the cardiac system were included. In addition, Chaos theory is a field of study in mathematics, with applications in several disciplines including physics, engineering, economics, biology, and philosophy. Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions, an effect which is popularly referred to as the butterfly effect. The goal was to investigate the influence of gender, age and day-night variation on these nonlinear HRV parameters. Numerical titration yielded similar information as other nonlinear HRV parameters do. However, it does not require long and cleaned data and therefore applicable on short (15 minutes) noisy time series. A higher nonlinear behavior was observed during the night while nonlinear heart rate fluctuations decline with increasing age. Our results support the involvement of the autonomic nervous system in the generation of nonlinear and complex heart rate dynamics. 

Moderate and heavy metabolic stress interval training improve arterial stiffness and heart rate dynamics in humans

Traditional continuous aerobic exercise training attenuates age-related increases of arterial stiffness, however, training studies have not determined whether metabolic stress impacts these favourable effects. Twenty untrained healthy participants (n = 11 heavy metabolic stress interval training, n = 9 moderate metabolic stress interval training) completed 6 weeks of moderate or heavy intensity interval training matched for total work and exercise duration. Carotid artery stiffness, blood pressure contour analysis, and linear and non-linear heart rate variability were assessed before and following training. Overall, carotid arterial stiffness was reduced (p < 0.01), but metabolic stress-specific alterations were not apparent. There was a trend for increased absolute high-frequency (HF) power (p = 0.10) whereas both absolute low-frequency (LF) power (p = 0.05) and overall power (p = 0.02) were increased to a similar degree following both training programmes. Non-linear heart rate dynamics such as detrended fluctuation analysis ({| {1 - alpha_{1} }|}) also improved (p > 0.05). This study demonstrates the effectiveness of interval training at improving arterial stiffness and autonomic function, however, the metabolic stress was not a mediator of this effect. In addition, these changes were also independent of improvements in aerobic capacity, which were only induced by training that involved a high metabolic stress.

Heart Rate Variability and Non-Linear Dynamics in Risk Stratification

The time-domain measures and power–spectral analysis of heart rate variability (HRV) are classic conventional methods to assess the complex regulatory system between autonomic nervous system and heart rate and are most widely used. There are abundant scientific data about the prognostic significance of the conventional measurements of HRV in patients with various conditions, particularly with myocardial infarction. Some studies have suggested that some newer measures describing non-linear dynamics of heart rate, such as fractal measures, may reveal prognostic information beyond that obtained by the conventional measures of HRV. An ideal risk indicator could specifically predict sudden arrhythmic death as the implantable cardioverter-defibrillator (ICD) therapy can prevent such events. There are numerically more sudden deaths among post-infarction patients with better preserved left ventricular function than in those with severe left ventricular dysfunction. Recent data support the concept that HRV measurements, when analyzed several weeks after acute myocardial infarction, predict life-threatening ventricular tachyarrhythmias in patients with moderately depressed left ventricular function. However, well-designed prospective randomized studies are needed to evaluate whether the ICD therapy based on the assessment of HRV alone or with other risk indicators improves the patients’ prognosis. Several issues, such as the optimal target population, optimal timing of HRV measurements, optimal methods of HRV analysis, and optimal cutpoints for different HRV parameters, need clarification before the HRV analysis can be a widespread clinical tool in risk stratification.