Nonlinear Heart Research Articles

Heart rate variability metrics for fine-grained stress level assessment

Background and Objectives: In spite of the existence of a multitude of techniques that allow the estimation of stress from physiological indexes, its fine-grained assessment is still a challenge for biomedical engineering. The short-term assessment of stress condition overcomes the limits to stress characterization with long blocks of time and allows to evaluate the behaviour change in real-world settings and also the stress level dynamics. The aim of the present study was to evaluate time and frequency domain and nonlinear heart rate variability (HRV) metrics for stress level assessment using a short-time window. Methods: The electrocardiogram (ECG) signal from 14 volunteers was monitored using the Vital JacketTM while they performed the Trier Social Stress Test (TSST) which is a standardized stress-inducing protocol. Window lengths from 220 s to 50 s for HRV analysis were tested in order to evaluate which metrics could be used to monitor stress levels in an almost continuous way. Results: A sub-set of HRV metrics (AVNN, rMSSD, SDNN and pNN20) showed consistent differences between stress and non-stress phases, and showed to be reliable parameters for the assessment of stress levels in short-term analysis. Conclusions: The AVNN metric, using 50 s of window length analysis, showed that it is the most reliable metric to recognize stress level across the four phases of TSST and allows a fine-grained analysis of stress effect as an index of psychological stress and provides an insight into the reaction of the autonomic nervous system to stress.

Heart rate variability based on risk stratification for type 2 diabetes mellitus.

ABSTRACTObjective To evaluate heart rate variability among adults with different risk levels for type 2 diabetes mellitus.Methods The risk for type 2 diabetes mellitus was assessed in 130 participants (89 females) based on the questionnaire Finnish Diabetes Risk Score and was classified as low risk (n=26), slightly elevated risk (n=41), moderate risk (n=27) and high risk (n=32). To measure heart rate variability, a heart-rate monitor Polar S810i® was employed to obtain RR series for each individual, at rest, for 5 minutes, followed by analysis of linear and nonlinear indexes.Results The groups at higher risk of type 2 diabetes mellitus had significantly lower linear and nonlinear heart rate variability indexes.Conclusion The individuals at high risk for type 2 diabetes mellitus have lower heart rate variability.

First-night effect on cardiac autonomic function in different female reproductive states.

Decreases in heart rate variability, a marker of autonomic nervous system function, are associated with increased cardiovascular mortality. Heart rate variability increases in non-rapid eye movement sleep, peaking in slow-wave sleep. Therefore, decreasing the amount of deep sleep, for example, by introducing patients to a sleep laboratory environment, could decrease heart rate variability, increasing cardiovascular risk. We studied four groups of women with no previous sleep laboratory experience: young [n=11, 23.1 (0.5) years]; perimenopausal [n=15, 48.0 (0.4) years]; postmenopausal without hormone therapy [n=22, 63.4 (0.8) years]; and postmenopausal on hormone therapy [n=16, 63.1 (0.9) years], using a cross-sectional design. Polysomnography including electrocardiogram was performed over two consecutive nights. Heart rate variability was assessed overnight, and the first-night effect on heart rate variability was analysed. Furthermore, correlations between heart rate variability and sleep variables were analysed. Using combined groups, only minor changes were observed in non-linear heart rate variability, indicating increased parasympathetic tone from the first to the second night. No group differences in first-night effect were seen. Heart rate variability and sleep variables were not significantly correlated. Heart rate variability decreased with increasing age, and it was lowest in the postmenopausal women on hormone therapy. We conclude that a first night in a sleep laboratory elicits only minimal changes in overnight vagally mediated non-linear heart rate variability in women irrespective of reproductive state. This finding warrants further analyses in different sleep stages, but suggests that changes in sleep architecture per se do not predict the autonomic strain of a poor night.

Depression and cardiac dysautonomia in eating disorders.

Individuals with eating disorder (ED) are at an increased risk of cardiac arrhythmias due to cardiac dysautonomia, which may be exacerbated if depression is also present. The aim of the study was to use heart rate analysis as a marker for cardiac dysautonomia in patients with eating disorders and depression as a comorbidity. Clinical data, including presence of depression, was obtained from all participants. A three-lead ECG was used to determine interbeat intervals, and these were analyzed using time domain, frequency domain, and nonlinear heart rate variability measures. Thirty ED patients and 44 healthy controls participated in the research. The presence of depression was associated with additional decreased time domain (RMSSD 36.8 ± 26 vs. 22.9 ± 12.3; p < 0.05), frequency domain (HF power 788 ± 1075 vs. 279 ± 261; p < 0.05), and nonlinear domain (DFAα2 0.82 ± 0.1 vs. 0.97 ± 0.1; p < 0.01) which results in the ED group compared to patients with no depression. The presence of depression in ED patients decreased HRV even further compared to the non-depressed patient group and controls, suggesting that higher vigilance and a holistic treatment approach may be required for these patients to avoid cardiac arrhythmia complications.

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

Heart rate complexity analysis in Brugada syndrome during physical stress testing

Symptoms such as ventricular arrhythmias in Brugada syndrome (BS) typically occur at rest, especially during sleep, suggesting that the autonomic nervous system (ANS) function may be relevant in the arrhythmogenesis of the disease. The aim of this work was to assess the ANS response captured by nonlinear heart rate variability (HRV) measures in 69 patients diagnosed with BS, who underwent a standardized physical stress test. Heart rate complexity (HRC) was evaluated by the power-law scaling analysis (β slope) during rest, exercise, recovery and rest post-recovery, in order to discriminate between symptomatic and asymptomatic BS patients. Symptomatic patients showed a significant reduction in HRC in comparison to asymptomatic subjects, after exertion (p = 0.015); during the whole recovery period (p = 0.023), and in particular within the passive recovery phase (p = 0.025), as well as during rest post-recovery (p = 0.022). Based on these results, symptoms could be associated with a lower ANS complexity during the stress test stages where parasympathetic activity is predominant. Therefore, the proposed HRV indicators could be of help in the risk stratification of asymptomatic patients.

Correlation between Decreased Parasympathetic Activity and Reduced Cerebrovascular Reactivity in Patients with Lacunar Infarct.

Reduced cerebrovascular reactivity (CVR) was found in patients with recent lacunar infarct. However, its mechanisms were controversial. The breath holding maneuver as a vasodilatory stimulus is clinically useful for an estimation of cerebrovasomotor reactivity in well co-operative patients. Patients with lacunar infarct have no higher cortical dysfunction and remain well co-operation. The breath holding maneuver is feasible and safe to perform in patients with lacunar infarct. Autonomic nervous system regulates systemic vascular activity. Regulation of autonomic function to cerebrovascular reactivity has been reported in the literature. We examined the correlation between autonomic functions with frequency and nonlinear heart rate variability (HRV) and cerebrovascular reactivity in patients with lacunar infarct by application of breath holding maneuver. Fifteen patients with lacunar infarct (8 women, age 65.6 ± 13.61) and 16 healthy controls (11 women, age 27.33 ± 3.85) were continuously monitored at baseline before maneuver (basal phase), during CVR induction (experimental phase) with breath holding maneuver and after maneuver (recovery phase), for arterial blood pressure (ABP), electrocardiography (EKG), mean cerebral blood flow velocity (mCBFV) of middle cerebral arteries (MCA) by transcranial doppler (TCD). The short term-one minute HRV was analyzed from EKG signals for low frequency (LF)/ high frequency (HF) ratio, nonlinear of standard deviation 1 (SD1), standard deviation 2 (SD2), cardiac Sample Entropy (SampEn) and Shannon Entropy. Significant increasing in mCBFV, LF/HF ratio, SD2/SD1, Shannon Entropy and inversely decreasing SampEn during breath holding maneuver compared with baseline were found in both groups (p<0.05). The trend of cerebrovascular reactivity is similar in both groups. However, there were differences of mCBFV, systolic blood pressure (SysBP) in the whole phases (basal, experiment and recovery) between patients and controls (p<0.05). Less scattered signals of SD1 with low value in patient group were illustrated from Poincaré (p<0.05). This indicated less degree of parasympathetic drive in the patients compared to the controls. Moreover, significant positive correlation between systolic bloods pressure and mCBFV in patients suggests impact of autonomic control and cerebral blood flow on the patho-physiological mechanism of vasodilatation, triggered by hypercapnia from breath holding maneuver in patients with lacunar infarct. Reduction of cerebrovascular reactivity in patients with lacunar infarct may relate with decreased parasympathetic activity. Further study is required to demonstrate whether these findings mean mechanisms of lacunar infarct or the effect of hypertensive response.

An Exercise Health Simulation Method Based on Integrated Human Thermophysiological Model.

Research of healthy exercise has garnered a keen research for the past few years. It is known that participation in a regular exercise program can help improve various aspects of cardiovascular function and reduce the risk of suffering from illness. But some exercise accidents like dehydration, exertional heatstroke, and even sudden death need to be brought to attention. If these exercise accidents can be analyzed and predicted before they happened, it will be beneficial to alleviate or avoid disease or mortality. To achieve this objective, an exercise health simulation approach is proposed, in which an integrated human thermophysiological model consisting of human thermal regulation model and a nonlinear heart rate regulation model is reported. The human thermoregulatory mechanism as well as the heart rate response mechanism during exercise can be simulated. On the basis of the simulated physiological indicators, a fuzzy finite state machine is constructed to obtain the possible health transition sequence and predict the exercise health status. The experiment results show that our integrated exercise thermophysiological model can numerically simulate the thermal and physiological processes of the human body during exercise and the predicted exercise health transition sequence from finite state machine can be used in healthcare.

Linear and non-linear heart rate metrics for the assessment of anaesthetists’ workload during general anaesthesia

BackgroundExcessive workload may impact the anaesthetists’ ability to adequately process information during clinical practice in the operation room and may result in inaccurate situational awareness and performance. This exploratory study investigated heart rate (HR), linear and non-linear heart rate variability (HRV) metrics and subjective ratings scales for the assessment of workload associated with the anaesthesia stages induction, maintenance and emergence. MethodsHR and HRV metrics were calculated based on five min segments from each of the three anaesthesia stages. The area under the receiver operating characteristics curve (AUC) of the investigated metrics was calculated to assess their ability to discriminate between the stages of anaesthesia. Additionally, a multiparametric approach based on logistic regression models was performed to further evaluate whether linear or non-linear heart rate metrics are suitable for the assessment of workload. ResultsMean HR and several linear and non-linear HRV metrics including subjective workload ratings differed significantly between stages of anaesthesia. Permutation Entropy (PeEn, AUC=0.828) and mean HR (AUC=0.826) discriminated best between the anaesthesia stages induction and maintenance. In the multiparametric approach using logistic regression models, the model based on non-linear heart rate metrics provided a higher AUC compared with the models based on linear metrics. ConclusionsIn this exploratory study based on short ECG segment analysis, PeEn and HR seem to be promising to separate workload levels between different stages of anaesthesia. The multiparametric analysis of the regression models favours non-linear heart rate metrics over linear metrics.

Arterial Stiffness and Autonomic Modulation After Free-Weight Resistance Exercises in Resistance Trained Individuals.

Kingsley, JD, Mayo, X, Tai, YL, and Fennell, C. Arterial stiffness and autonomic modulation after free-weight resistance exercises in resistance trained individuals. J Strength Cond Res 30(12): 3373-3380, 2016-We investigated the effects of an acute bout of free-weight, whole-body resistance exercise consisting of the squat, bench press, and deadlift on arterial stiffness and cardiac autonomic modulation in 16 (aged 23 ± 3 years; mean ± SD) resistance-trained individuals. Arterial stiffness, autonomic modulation, and baroreflex sensitivity (BRS) were assessed at rest and after 3 sets of 10 repetitions at 75% 1-repetition maximum on each exercise with 2 minutes of rest between sets and exercises. Arterial stiffness was analyzed using carotid-femoral pulse wave velocity (cf-PWV). Linear heart rate variability (log transformed [ln] absolute and normalized units [nu] of low-frequency [LF] and high-frequency [HF] power) and nonlinear heart rate complexity (Sample Entropy [SampEn], Lempel-Ziv Entropy [LZEn]) were measured to determine autonomic modulation. BRS was measured by the sequence method. A 2 × 2 repeated measures analysis of variance (ANOVA) was used to analyze time (rest, recovery) across condition (acute resistance exercise, control). There were significant increases in cf-PWV (p = 0.05), heart rate (p = 0.0001), normalized LF (LFnu; p = 0.001), and the LF/HF ratio (p = 0.0001). Interactions were also noted for ln HF (p = 0.006), HFnu (p = 0.0001), SampEn (p = 0.001), LZEn (p = 0.005), and BRS (p = 0.0001) such that they significantly decreased during recovery from the resistance exercise compared with rest and the control. There was no effect on ln total power, or ln LF. These data suggest that a bout of resistance exercise using free-weights increases arterial stiffness and reduces vagal activity and BRS in comparison with a control session. Vagal tone may not be fully recovered up to 30 minutes after a resistance exercise bout.

Influence of Heart Rate in Non-linear HRV Indices as a Sampling Rate Effect Evaluated on Supine and Standing.

The purpose of this study is to characterize and attenuate the influence of mean heart rate (HR) on nonlinear heart rate variability (HRV) indices (correlation dimension, sample, and approximate entropy) as a consequence of being the HR the intrinsic sampling rate of HRV signal. This influence can notably alter nonlinear HRV indices and lead to biased information regarding autonomic nervous system (ANS) modulation. First, a simulation study was carried out to characterize the dependence of nonlinear HRV indices on HR assuming similar ANS modulation. Second, two HR-correction approaches were proposed: one based on regression formulas and another one based on interpolating RR time series. Finally, standard and HR-corrected HRV indices were studied in a body position change database. The simulation study showed the HR-dependence of non-linear indices as a sampling rate effect, as well as the ability of the proposed HR-corrections to attenuate mean HR influence. Analysis in a body position changes database shows that correlation dimension was reduced around 21% in median values in standing with respect to supine position (p < 0.05), concomitant with a 28% increase in mean HR (p < 0.05). After HR-correction, correlation dimension decreased around 18% in standing with respect to supine position, being the decrease still significant. Sample and approximate entropy showed similar trends. HR-corrected nonlinear HRV indices could represent an improvement in their applicability as markers of ANS modulation when mean HR changes.

Alterations in autonomic cardiac modulation in response to normobaric hypoxia

Purpose: The present study aimed to determine if autonomic cardiac modulation was influenced by acute exposure to normobaric hypoxia. Method: Ten healthy male lowland dwellers completed five block-randomised single-blinded, crossed-over acute exposures to a normobaric hypoxic environment, each separated by 24 hours’ recovery (20.3%, 17.4%, 14.5%, 12.0% and 9.8% FIO2). Supine recordings were made of arterial oxygen saturation and electrocardiogram (ECG). RR intervals from the ECG trace were analysed for time (SDNN, lnrMSSD), frequency (lnVLF, lnLF, lnHF, lnTP, LFnu, and HFnu), and nonlinear (DFA-α1 and SampEn) heart rate variability components. Results: A significant reduction in arterial SaO2 occurred with reduced FIO2, along with a rise in heart rate (Cohen’s d = 1.16, 95% Confidence Interval [2.64–6.46]), significant at 9.8% FIO2. A decrease in autonomic cardiac modulation was also found as shown by a statistically significant (at 9.8% FIO2) decrease in lnTP (d = 1.84 [1.74–1.94]), and SampEn (d = 0.98 [0.83–1.12]) and an increase in DFA-α1 (d = 0.72 [0.60–0.84]) from normoxia at 9.8% FIO2. Conclusion: The decrease in variability indicated a reduction in autonomic cardiac modulation. There appears to be a threshold ∼9.8% FIO2 (∼6000 m equiv.), below which significant alterations in autonomic control occur.

Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index

In our previous work, we have developed a sudden cardiac death index (SCDI) using electrocardiogram (ECG) signals that could effectively predict the occurrence of SCD four minutes before the onset. Thus, the prediction of SCD before its onset by using heart rate variability (HRV) signals is a worthwhile task for further investigation. Therefore, in this paper, a new novel methodology to automatically classify the HRV signals of normal and subjects at risk of SCD by using nonlinear techniques has been presented. In this study, we have predicted SCD by analyzing four-minutes of HRV signals (separately for each one-minute interval) prior to SCD occurrence by using nonlinear features such as Renyi entropy (REnt), fuzzy entropy (FE), Hjorth's parameters (activity, mobility and complexity), Tsallis entropy (TEnt), and energy features of discrete wavelet transform (DWT) coefficients. All the clinically significant features obtained are ranked using their t-value and fed to classifiers such as K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). In this work, we have achieved an accuracy of 97.3%, 89.4%, 89.4%, and 94.7% for prediction of SCD one, two, three, and four minutes prior to the SCD onset respectively using SVM classifier. Furthermore, we have also developed a novel SCD Index (SCDI) by using nonlinear HRV signal features to classify the normal and SCD prone HRV signals. Our proposed technique is able to identify the person at risk of developing SCD four minutes earlier, thereby providing sufficient time for the clinicians to respond with treatment in Intensive Care Units (ICU). Thus, this proposed technique can thus serve as a valuable tool for increasing the survival rate of many cardiac patients.

Non-invasive electrocardiographic assessments of cardiac autonomic modulation in individuals with spinal cord injury.

Review. The purpose of this review is to discuss the utility of linear and non-linear heart rate variability (HRV) as well as the QT-variability index (QTVI) as indices of cardiac autonomic control in individuals with spinal cord injury (SCI). Brock University, Department of Kinesiology, St Catharines, Ontario, Canada. Brock-Niagara Centre for Health and Well-being, St Catharines, Ontario, Canada. Literature review. Non-invasive markers determined from the electrocardiogram, such as linear and non-linear HRV, and, more recently, the QTVI have all shown some promise as indices of cardiac autonomic regulation in the SCI population. However, there are inconsistencies in the literature that call to question their true validity in this regard. Studies using pharmacological blockade, sympathetic manoeuvres and exercise suggest that both linear and non-linear HRV reflect cardiac parasympathetic activity, whereas their ability to quantify cardiac sympathetic outflow remains uncertain. The QTVI, although a novel method, correlates with both limbs of the autonomic nervous system and therefore may hold value as a measure of both cardiac sympathetic and parasympathetic activity in individuals with SCI; however, more research is required to confirm its utility.

Severe Cardiac Autonomic Derangement and Altered Ventricular Repolarization Pave the Way to Postoperative Atrial Fibrillation.

Postoperative atrial fibrillation (POAF) is a frequent complication after heart surgery. It has been shown that cardiac autonomic derangement plays a significant role in the genesis of atrial fibrillation (AF) and that AF might also be promoted by altered repolarization. Thus, the aim of our study was to determine the levels of cardiac autonomic modulation and repolarization properties in patients developing POAF. Seventy-nine patients scheduled for aortic and/or coronary artery bypass grafting surgery with cardiopulmonary bypass were enrolled prospectively. High-resolution 20-minute electrocardiogram recordings were obtained day before surgery to determine P, PR, QT, and QTc intervals, as well as linear (time and frequency domain) and nonlinear heart rate variability parameters (fractal dimension and detrended fluctuation analysis). QTc interval was calculated using Framingham correction. Twenty-nine patients developed POAF (AF group), and 50 did not (non-AF group). Groups were similar regarding demographics, surgery type, and perioperative characteristics, except for older age in the AF group. QT and QTc intervals (Framingham) were longer in the AF group [442 (44) vs 422 (28) milliseconds, P = 0.018; and 448 (44) vs 431 (24) milliseconds, P = 0.031 and P = 0.019, respectively]. Time domain heart rate variability parameter PNN50 (percentage of pairs of adjacent NN intervals differing >50 milliseconds) was higher [14% (21%) vs 8% (16%), P = 0.015], and nonlinear parameter detrended fluctuation analysis α2 was lower in the AF group [0.81 (0.21) vs 0.91 (0.20), P = 0.031]. Profound cardiac autonomic derangement, suggestive of parasympathetic excessive modulation, exists preoperatively in patients inclined to POAF after cardiac surgery, whereby parameters PNN50 and α2 differentiated the AF from the non-AF group. Prolonged QTc intervals are associated with an increased risk of POAF.

Physical Fitness and Dehydration Influences on the Cardiac Autonomic Control of Fighter Pilots.

Physical fitness and dehydration are factors that may influence cardiac autonomic control. We aimed to verify the influence of these factors on cardiac autonomic control before, during, and after a flight. At the same time of day, 11 healthy fighter pilots recorded several 1-h bouts of heart rate (HR) activity during a non- (control) and a training flight day. Autonomic control of HR was examined via time domain and non-linear heart rate variability (HRV) analyses. The level of dehydration during the flight was evaluated by changes in hematocrit, while aerobic capacity, muscular strength, and body fatness were the physical fitness components evaluated. The flight induced a significant reduction in most parameters of HRV during flight time when compared to the control day. However, no differences were found between the days before the flight, while the root mean square of successive differences (RMSSD) of HR was the only parameter significantly reduced (11.05 ± 7.7%) after the flight. Significant correlations were observed between the sample entropy of HR during flight and aerobic capacity (r = 0.777) and body fatness (r = -0.617). Correlations between dehydration and changes in HRV (RMSSD and SD1) were also identified (r = -0.61 to -0.81). The current results demonstrated significant relationships between aerobic capacity, body fatness, and hydration status on autonomic control of HR during and after flights. No relationship to muscular strength was observed. Future studies may further elucidate the impact of these factors on pilot training in order to accommodate flight's stressors and enhance performance.

Adaptive correlation dimension method for analysing heart rate variability during the menstrual cycle.

Correlation dimension (CD) is used for analysing the chaotic behaviour of the nonlinear heart rate variability (HRV) time series. In CD, the autocorrelation function is used to calculate the time delay. However, it does not provide optimum values of time delays, which leads to an inaccurate estimation of the HRV between phases of the menstrual cycle. Thus, an adaptive CD method is presented here to calculate the optimum value of the time delay based upon the information content in the HRV signal. In the proposed method, the first step is to divide the HRV signal into overlapping windows. Afterwards, the time delay is calculated for each window based on the features of the signal. This procedure of finding the optimum time delay for each window is known as adaptive autocorrelation. Then, the CD for each window is calculated using optimum time delays. Finally, adaptive CD is calculated by averaging the CD of all windows. The proposed method is applied on two data sets: (i) the standard Physionet dataset and (ii) the dataset acquired using BIOPAC(®)MP150. The results show that the proposed method can accurately differentiate between normal and diseased subjects. Further, the results prove that the proposed method is more accurate in detecting HRV variations during the menstrual cycles of 74 young women in lying and standing postures. Three statistical parameters are used to find the effectiveness of adaptive autocorrelation in calculating time delays. The comparative analysis validates the superiority of the proposed method over detrended fluctuation analyses and conventional CD.

Non-Linear Heart Rate Variability Indices in the Frequent Hemodialysis Network Trials of Chronic Hemodialysis Patients

Background: Non-linear heart rate variability (HRV) indices were hypothesized to correlate with cardiac function, fluid overload and physical performance in hemodialysis patients. Methods: Twenty-four-hour Holter electrocardiograms were recorded in patients enrolled in the Frequent Hemodialysis Network (FHN) Daily Dialysis Trial. Correlations between non-linear HRV indices and left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV), extracellular volume (ECV)/total body water (TBW) ratio, the SF-36 Physical Health Composite (PHC) and Physical Functioning (PF) scores were tested. Results: We studied 210 subjects (average age 49.8 ± 13.5 years, 62% men, 42% diabetics). In non-diabetic patients, multiscale entropy (MSE) slope sample entropy (SampEn) and approximate entropy (ApEn) correlated positively with LVEF, PF and PHC and inversely with LVEDV and ECV/TBW. Spectral power slope correlated positively with ECV/TBW (r = 0.27). Irregularity measures (MSE ApEn and MSE SampEn) correlated positively with LVEDV (r = 0.19 and 0.20). Conclusion: Non-linear HRV indices indicated an association between a deteriorated heart rate regulatory system and impaired cardiac function, fluid accumulation and poor physical condition.

Effects of breathing patterns and light exercise on linear and nonlinear heart rate variability.

Despite their use in cardiac risk stratification, the physiological meaning of nonlinear heart rate variability (HRV) measures is not well understood. The aim of this study was to elucidate effects of breathing frequency, tidal volume, and light exercise on nonlinear HRV and to determine associations with traditional HRV indices. R-R intervals, blood pressure, minute ventilation, breathing frequency, and respiratory gas concentrations were measured in 24 healthy male volunteers during 7 conditions: voluntary breathing at rest, and metronome guided breathing (0.1, 0.2 and 0.4 Hz) during rest, and cycling, respectively. The effect of physical load was significant for heart rate (HR; p < 0.001) and traditional HRV indices SDNN, RMSSD, lnLFP, and lnHFP (p < 0.01 for all). It approached significance for sample entropy (SampEn) and correlation dimension (D2) (p < 0.1 for both), while HRV detrended fluctuation analysis (DFA) measures DFAα1 and DFAα2 were not affected by load condition. Breathing did not affect HR but affected all traditional HRV measures. D2 was not affected by breathing; DFAα1 was moderately affected by breathing; and DFAα2, approximate entropy (ApEn), and SampEn were strongly affected by breathing. DFAα1 was strongly increased, whereas DFAα2, ApEn, and SampEn were decreased by slow breathing. No interaction effect of load and breathing pattern was evident. Correlations to traditional HRV indices were modest (r from -0.14 to -0.67, p < 0.05 to <0.01). In conclusion, while light exercise does not significantly affect short-time HRV nonlinear indices, respiratory activity has to be considered as a potential contributor at rest and during light dynamic exercise.

Heart Rate Regulation with Different Heart rate Reference Profiles for Electric Bicycle Riders

In this paper, we propose a new nonlinear control strategy for electric bicycles that adjusts the motor assistance automatically to maintain a desired heart rate level of the cyclist. The control algorithm uses the current heart rate, torque and cadence of the rider as inputs, and adjusts the assistance of the motor adaptively. The controller consists of feedforward as well as feedback components: Whereas the heart rate is the feedback component, the system's feedforward component is the rider's estimated torque output at the desired heart rate level. Environmental changes like slope or headwind are modeled as disturbances that the controller has to reject. We conduct test rides with one subject at different intensity levels to develop and validate a nonlinear heart rate response model of the bicycle rider. The model uses the power output of the cyclist as an input and takes immediate and gradual responses of the heart rate into account. We use a commercial electric bicycle with a front wheel hub motor to test the controller design. The bicycle is modified to measure torque output and pedalling frequency of the rider and to control the motor torque. The heart rate is measured by a chest strap. Data processing and controller design were performed on a laptop. We present simulation as well as experimental results for different reference heart rate profiles to verify the performance of the controller.