Linear Heart Rate Variability Research Articles

Recovery of cardiac electrophysiological alterations by heart rate complexity based on multiscale entropy following liver transplantation

Autonomic nervous dysfunction is a known cardiac sequalae in patients with end-stage liver disease and is associated with a poor prognosis. Heart rate analysis using nonlinear models such as multiscale entropy (MSE) or complexity may identify marked changes in these patients where conventional heart rate variability (HRV) measurements do not. To investigate the application of heart rate complexity (HRC) based on MSE in liver transplantation settings. Thirty adult recipients of elective living donor liver transplantation were enrolled. HRV parameters using conventional HRV analysis and HRC analysis were obtained at the following time points: (1) 1 day before surgery, (2) postoperative day (POD) 7, (3) POD 14, (4) POD 90, and (5) POD 180. Preoperatively, patients with MELD score ≥ 25 had significantly lower HRC compared to patients with lower MELD scores. This difference in HRC disappeared by POD 7 following liver transplantation and subsequent analyses at POD 90 and 180 continued to show no significant difference. Our results indicated a significant negative correlation between HRC based on MSE analysis and liver disease severity preoperatively, which may be more sensitive than conventional linear HRV analysis. HRC in patients with MELD score ≧ 25 improved over time and became comparable to those with MELD < 25 as early as in 7 days.

Heart rhythm complexity analysis in patients with inferior ST-elevation myocardial infarction

Heart rhythm complexity (HRC), a subtype of heart rate variability (HRV), is an important tool to investigate cardiovascular disease. In this study, we aimed to analyze serial changes in HRV and HRC metrics in patients with inferior ST-elevation myocardial infarction (STEMI) within 1 year postinfarct and explore the association between HRC and postinfarct left ventricular (LV) systolic impairment. We prospectively enrolled 33 inferior STEMI patients and 74 control subjects and analyzed traditional linear HRV and HRC metrics in both groups, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We also analyzed follow-up postinfarct echocardiography for 1 year. The STEMI group had significantly lower standard deviation of RR interval (SDNN), and DFAα2 within 7 days postinfarct (acute stage) comparing to control subjects. LF power was consistently higher in STEMI group during follow up. The MSE scale 5 was higher at acute stage comparing to control subjects and had a trend of decrease during 1-year postinfarct. The MSE area under scale 1–5 showed persistently lower than control subjects and progressively decreased during 1-year postinfarct. To predict long-term postinfarct LV systolic impairment, the slope between MSE scale 1 to 5 (slope 1–5) had the best predictive value. MSE slope 1–5 also increased the predictive ability of the linear HRV metrics in both the net reclassification index and integrated discrimination index models. In conclusion, HRC and LV contractility decreased 1 year postinfarct in inferior STEMI patients, and MSE slope 1–5 was a good predictor of postinfarct LV systolic impairment.

Heart rate variability at rest and in response to stress: Comparative study between hemodialysis and peritoneal dialysis patients.

Cardiac arrhythmias and sudden death are the leading causes of mortality in end-stage kidney disease (ESKD). Autonomic nervous system (ANS) dysfunction contributes to this arrhythmogenic background. This study compared heart rate variability (HRV) indices between hemodialysis (HD) and peritoneal dialysis (PD) patients, both at rest and in response to mental and physical stimulation maneuvers. Thirty-four HD and 34 PD patients matched for age, sex, and dialysis vintage, and 17 age- and sex-matched controls were studied. ANS function was examined by linear and non-linear HRV indices. Heart rate was recorded continuously (Finometer-PRO) at rest and during ANS maneuvers (orthostatic, mental-arithmetic, sit-to-stand, handgrip exercise tests). At rest, no significant differences between HD and PD were observed in HRV (root mean square of successive differences [RMSSD]: HD = 57.1 ± 81.1 vs PD = 69.6 ± 113.4 ms; P = 0.792), except for detrended fluctuation analysis (DFA-α1) (HD = 0.87 ± 0.40 vs PD = 0.70 ± 0.20; P = 0.047). DFA-α1 was significantly lower in PD than controls (1.00 ± 0.33; P < 0.05). All HRV indices during the mental-arithmetic test (RMSSD: HD = 128.2 ± 346.0 vs PD = 87.5 ± 150.0 ms; P = 0.893) and the physical stress tests were similar between HD and PD. The standard deviation along the line-of-identity (SD2)/the standard deviation perpendicular to the line-of-identity (SD1) ratio during mental-arithmetic was marginally lower in HD and significantly lower in PD than controls (PD = 1.31 ± 0.47 vs controls = 1.79 ± 0.64; P < 0.05). Both dialysis groups presented similar patterns in HRV responses during orthostatic and handgrip exercise tests. After the sit-to-stand, RMSSD, SD1, SD2, and DFA-α2 were higher compared to rest only in HD (RMSSD = 57.1 ± 81.1 vs 126.7 ± 185.7 ms; P = 0.028), suggesting a greater difficulty of HD patients in recovering normal ANS function in response to physical stress. In conclusion, HRV indices at rest and after mental and physical stimulation did not differ between HD and PD; however, the ANS responses following the sit-to-stand test were more impaired in HD. These findings suggest that ANS dysfunction is not largely affected by dialysis modality, but small differences in normal ANS recovery may exist.

Linear and non‑linear indices of vagal nerve in relation to sex and inflammation in patients with Covid‑19.

Hyperinflammation is one of the most important pathophysiological risk factors for poor prognosis in patients with coronavirus disease-2019 (Covid-19). Low vagal neuro-immune modulation can lead into this kind of immune dysregulation. The association between vagal activity, sex and inflammatory markers were investigated in patients with Covid-19. A total of 19 patients with Covid-19 were included in the present study. Vagus nerve activity was indexed by heart rate variability (HRV) derived from electrocardiogram at hospital admission. Linear HRV parameters included the root mean square of successive RR interval differences (RMSSD) and high-frequency HRV (HF-HRV), while non-linear parameters included 2 UV%. Immune/inflammatory parameters included C-reactive protein (CRP), interleukin-6 (IL-6), neutrophil/lymphocyte ratio (NLR), systemic inflammatory index (SII), and procalcitonin (PCT). It has been revealed that both linear HRV indices HF-HRV and RMSSD, are significantly negatively correlated with CRP and IL-6, independent of age. The non-linear index of 2 UV% is significantly negatively correlated with NLR and SII, which reflect subtle changes in the response of immunocompetent cells. Patients that received high-flow nasal oxygen therapy had significantly higher IL-6 and CRP levels and lower levels of HF-HRV and RMSSD. These patients also had a significantly longer length of stay in hospital (LOS) than patients receiving low-flow oxygen therapy. Men had higher plasma PCT levels and longer LOS in hospital than women, and PCT statistically explained (mediated) the association between sex and LOS. The present study showed different correlations of linear and non-linear vagal indexes of HRV and inflammatory markers in patients with Covid-19. Significant sex differences in certain inflammatory markers were also observed, which may very well verify previous findings of poor prognosis in men with Covid-19. HRV reflects a continuous interaction between the sympathetic and parasympathetic autonomic nervous systems, which are affected by mental or physical stress, and certain disease states. The increased sympathetic and decreased parasympathetic vagal tone contribute to a higher risk of diseases associated with inflammation, cardiovascular disease, cancer, pulmonary diseases and other pathologies, including infectious diseases such as Covid-19. The present study showed that higher RMSSD (a marker of vagal activity) in Covid-19 patients is associated with lower levels of inflammatory biomarkers, a lower need for treatment and is negatively correlated with intensive care unit admission, leading to a shorter hospital stay. These findings support the idea that activation of vagus nerve may help certain Covid-19 patients by reducing the cytokine storm and excessive inflammation.

Fractal complexity alternations in paroxysmal atrial fibrillation patients with and without recurrence after pulmonary vein isolation.

Pulmonary vein isolation (PVI) is a cornerstone therapy for paroxysmal atrial fibrillation (PAF). The variations in nonlinear heart rate variability (HRV) between patients with and without recurrences remain unclear. We aimed to characterize the nonlinear HRV before and after PVI in patients with and without recurrence. Twenty-five drug-refractory PAF patients (56.0 ± 9.1 years old, 20 males) who received PVI were enrolled. Holter electrocardiography were performed before, 1-3, and 6-12 months after PVI. After 8.2 ± 2.5 months of follow-ups after PVI, patients were divided into two groups: the recurrence (n = 8) and non-recurrence (n = 17) groups. Linear and nonlinear HRV variables were analyzed, including the Poincaré Plot analysis and the Detrended Fluctuation Analysis (DFA). The non-recurrence group, but not the recurrence group, had decreased high-frequency component (HF), the root mean square of successive RR interval differences (RMSSD), and the Poincaré Plot index SD1 1-3 months after PVI and increased DFAslope2 6-12 months after PVI. The non-recurrence group's LF/HF ratio and DFAslope1 decreased significantly 1-3 and 6-12 months after PVI, respectively, whereas there was no significant change in the recurrence group after PVI. Significantly reduced vagal tone 1-3 months after PVI, increased long-term fractal complexity 6-12 months after PVI, and decreased sympathetic tone as well as short-term fractal complexity 1-3 and 6-12 months after PVI led to a better AF-free survival after PVI. These findings suggest that neuromodulation and heart rate dynamics play crucial roles in AF recurrence following PVI.

Identification and Classification of Physical Fatigue in Construction Workers Using Linear and Nonlinear Heart Rate Variability Measurements

Several studies have analyzed heart rate variability (HRV) using nonlinear methods, such as approximate entropy, the largest Lyapunov exponent, and correlation dimension in patients with cardiovascular disorders. However, few studies have used nonlinear methods to analyze HRV in order to determine the level of physical fatigue experienced by construction workers. As a result, to identify and categorize physical fatigue in construction workers, the current study examined the linear and nonlinear approaches of HRV analysis. Fifteen healthy construction workers (mean age, 33.2±6.9 years) were selected for this study. A textile-based wearable sensor monitored each participant’s HRV after they completed 60 min of bar bending and fixing tasks. At baseline, 15, 30, 45, and 60 min into the task, participants were given the Borg-20 to measure their subjective levels of physical fatigue. Nonlinear [e.g., R-R interval (RRI) variability, entropy, detrended fluctuation analysis] and linear (e.g., time- and frequency-domain) HRV parameters were extracted. Five machine learning classifiers were used to identify and discern different physical fatigue levels. The accuracy and validity of the classifier models were evaluated using 10-fold cross-validation. The classification models were developed by either combining or individualized HRV features derived from linear and nonlinear HRV analyses. In the individualized feature sets, time-domain features had the highest classification accuracy (92%) based on the random forest (RF) classifier. The combined feature (i.e., the time-domain and nonlinear features) sets showed the highest classification accuracy (93.5%) using the RF classifier. In conclusion, this study showed that both linear and nonlinear HRV analyses can be used to detect and classify physical fatigue in construction workers. This research offers important contributions to the industry by analyzing the variations in linear and nonlinear HRV parameters in response to construction tasks. This study demonstrates that HRV values changed significantly in response to physical work, indicating a change in the relative activity of cardiac autonomic functions as a result of fatigue. Using the ways in which HRV parameters vary in response to increased workloads provides a sensitive marker for contrasting construction workers with and without cardiovascular disease. It also allows the site manager to track how quickly workers fatigue, so that they can switch up their workload to reduce the likelihood that any one worker would get severely exhausted, or to suggest that workers who are already severely fatigued take a break to prevent further injury.

Continuous long-term heart rate variability and risk assessment in pulmonary hypertension

ObjectiveCurrent multimodal risk assessment for pulmonary hypertension (PH) has been redefined with a simplified assessment for follow-up in the new European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Follow-up risk...

HEMODIALYSIS EFFECTS ON AUTONOMIC FUNCTION: RESULTS OF LINEAR AND NON-LINEAR ANALYSIS OF HEART-RATE-VARIABILITY AT REST AND IN RESPONSE TO PHYSICAL AND MENTAL STRESS TESTS

Objective: Cardiac arrhythmias are the most common cause of death in hemodialysis. Autonomic dysfunction plays a central role in this arrhythmogenic background. Previous studies on hemodialysis-related changes in heart-rate-variability(HRV) give contradictory results. This study investigated HRV indices both at rest and in response to physical and mental stimulation maneuvers at multiple time-points around and during the hemodialysis procedure. Design and method: Autonomic function was assessed by linear and non-linear HRV indices at pre-dialysis, during-dialysis(3 equal time-periods), post-dialysis, and on the non-dialysis day in 36 hemodialysis patients. Continuous measurement of beat-by-beat heart-rate was recorded with Finometer-PRO(The Netherlands) at rest and after orthostatic, sit-to-stand, handgrip and mental-arithmetic test. Results: The RMSSD, SD1, and SD2 indices significantly increased during dialysis(early-HD, mid-HD, late-HD periods) compared with the pre-dialysis levels(p&lt;0.05) and returned to baseline post-dialysis(RMSSD: 54.39±83.73 vs 137.98±109.53* vs 119.85±97.34* vs 144.47±88.74* vs 85.82±121.43msec, *p&lt;0.05 vs pre-dialysis and post-dialysis). No differences were detected in the above indices between the pre-dialysis and non-dialysis day. However, post-dialysis, the HRV responses to orthostatic and sit-to-stand tests were more exaggerated than in the pre-dialysis measurements (p&lt;0.05). The HRV responses both at resting and physical tests in the non-dialysis day were similar to the pre-dialysis levels. HRV indices in mental-arithmetic test during hemodialysis were much higher than at the non-dialysis day(RMSSD: 77.05 (180.41) vs 19.75 (105.47)msec; p = 0.031) were detected. Conclusions: Hemodialysis causes marked changes in autonomic function. Resting HRV indices return to baseline post-dialysis, but HRV responses to physical stress remain exaggerated and return to baseline on the non-dialysis day. Detecting patients with significant autonomic dysfunction may help towards reduction of arrhythmia risk through individualized approaches.

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

Association of heartbeat complexity with survival in advanced non-small cell lung cancer patients.

Previous studies have shown that the predictive value of traditional linear (time domain and frequency domain) heart rate variability (HRV) for the survival of patients with advanced non-small cell lung cancer (NSCLC) is controversial. Nonlinear methods, based on the concept of complexity, have been used to evaluate HRV, providing a new means to reveal the physiological and pathological changes in HRV. This study aimed to assess the association between heartbeat complexity and overall survival in patients with advanced NSCLC. This study included 78 patients with advanced NSCLC (mean age: 62.0 ± 9.3 years). A 5-min resting electrocardiogram of advanced NSCLC patients was collected to analyze the following HRV parameters: time domain indicators, i.e., standard deviation of the normal-normal intervals (SDNN) and root mean square of successive interval differences (RMSSD); frequency domain indicators, i.e., total power (TP), low frequency power (LF), high frequency power (HF), and the ratio of LF to HF (LF/HF); nonlinear HRV indicators characterizing heartbeat complexity, i.e., approximate entropy (ApEn), sample entropy (SampEn), and recurrence quantification analysis (RQA) indexes: mean diagonal line length (Lmean), maximal diagonal line length (Lmax), recurrence rate (REC), determinism (DET), and shannon entropy (ShanEn). Univariate analysis revealed that the linear frequency domain parameter HF and nonlinear RQA parameters Lmax, REC, and DET were significantly correlated with the survival of advanced NSCLC patients (all p < 0.05). After adjusting for confounders in the multivariate analysis, HF, REC, and DET were found to be independent prognostic factors for the survival of patients with advanced NSCLC (all p < 0.05). There was an independent association between heartbeat complexity and survival in advanced NSCLC patients. The nonlinear analysis method based on RQA may provide valuable additional information for the prognostic stratification of patients with advanced NSCLC and may supplement the traditional time domain and frequency domain analysis methods.

Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU.

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.

Hemodialysis Effects on Autonomic Function: Results of Linear and Nonlinear Analysis of Heart Rate Variability at Rest and in Response to Physical and Mental Stress Tests

Introduction: Cardiac arrhythmias are the most common cause of death in hemodialysis. Autonomic dysfunction plays a central role in this arrhythmogenic background. Previous studies on hemodialysis-related changes in heart rate variability (HRV) give contradictory results. This study investigated HRV indices both at rest and in response to physical and mental stimulation maneuvers at multiple time-points around and during the hemodialysis procedure. Methods: Autonomic function was assessed by linear and nonlinear HRV indices at predialysis, during dialysis (3 equal time-periods), postdialysis, and on the nondialysis day in 36 hemodialysis patients. Continuous measurement of beat-by-beat heart rate was recorded with Finometer-PRO (The Netherlands) at rest and after orthostatic, sit-to-stand, handgrip, and mental-arithmetic test. Results: The RMSSD, SD1, and SD2 indices significantly increased during dialysis (early-HD, mid-HD, late-HD periods) compared with the predialysis levels (p < 0.05) and returned to baseline postdialysis (RMSSD: 54.39 ± 83.73 vs. 137.98 ± 109.53* vs. 119.85 ± 97.34* vs. 144.47 ± 88.74* vs. 85.82 ± 121.43msec, *p < 0.05 vs. predialysis and postdialysis). No differences were detected in the above indices between the predialysis and nondialysis day. However, postdialysis, the HRV responses to orthostatic and sit-to-stand tests were more exaggerated than in the predialysis measurements (p < 0.05). The HRV responses both at resting and physical tests in the nondialysis day were similar to the predialysis levels. HRV indices in the mental arithmetic test during hemodialysis were much higher than at the nondialysis day (RMSSD: 77.05 [180.41] versus 19.75 [105.47] msec; p = 0.031). Conclusions: Hemodialysis causes marked changes in the autonomic function. Resting HRV indices return to baseline postdialysis, but HRV responses to physical stress remain exaggerated and return to baseline on the nondialysis day. Detecting patients with significant autonomic dysfunction may help towards reduction of arrhythmia risk through individualized approaches.

Correlation between heart rate variability and polysomnography-derived scores of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is one of the most common sleep disorders and affects nearly a billion people worldwide. Furthermore, it is estimated that many patients with OSA are underdiagnosed, which contributes to the development of comorbidities, such as cardiac autonomic imbalance, leading to high cardiac risk. Heart rate variability (HRV) is a non-invasive, widely used approach to evaluating neural control of the heart. This study evaluates the relationship between HRV indices and the presence and severity of OSA. We hypothesize that HRV, especially the nonlinear methods, can serve as an easy-to-collect marker for OSA early risk stratification. Polysomnography (PSG) exams of 157 patients were classified into four groups: OSA-free (N = 26), OSA-mild (N = 39), OSA-moderate (N = 37), and OSA-severe (N = 55). The electrocardiogram was extracted from the PSG recordings, and a 15-min beat-by-beat series of RR intervals were generated every hour during the first 6h of sleep. Linear and nonlinear HRV approaches were employed to calculate 32 indices of HRV. Specifically, time- and frequency-domain, symbolic analysis, entropy measures, heart rate fragmentation, acceleration and deceleration capacities, asymmetry measures, and fractal analysis. Results with indices of sympathovagal balance provided support to reinforce previous knowledge that patients with OSA have sympathetic overactivity. Nonlinear indices showed that HRV dynamics of patients with OSA display a loss of physiologic complexity that could contribute to their higher risk of development of cardiovascular disease. Moreover, many HRV indices were found to be linked with clinical scores of PSG. Therefore, a complete set of HRV indices, especially the ones obtained by the nonlinear approaches, can bring valuable information about the presence and severity of OSA, suggesting that HRV can be helpful for in a quick diagnosis of OSA, and supporting early interventions that could potentially reduce the development of comorbidities.

Prefrontal cortex oxygenation and autonomic nervous system activity under transcutaneous auricular vagus nerve stimulation in adolescents.

The Neurovisceral Integration Model (NIM) proposes a complex interplay of visceral and neural structures that are crucial for adaptive responses to environmental demands. The aim of the present study was to investigate this circuitry using experimental manipulation via transcutaneous auricular vagus nerve stimulation (tVNS), measures of peripheral autonomic nervous system (ANS) activity and prefrontal cortex (PFC) oxygenation, quantified using functional near-infrared spectroscopy (fNIRS). In a sample of n=30 adolescents (age 14-17years), tVNS versus sham stimulation was applied each during a 15-minute stimulation phase in a within-subject-cross-randomized-design. Mean oxygenation of the PFC and functional connectivity were assessed using fNIRS. Additionally, heart rate variability (HRV), heart rate (HR), electrodermal activity (EDA), and saliva alpha-amylase (sAA) were assessed to quantify peripheral ANS activity. Using linear mixed-effects models, HRV increased (p<.0001) and HR (p<.0001) decreased during tVNS compared to sham. No effect on EDA or sAA was observed. PFC oxygenation increased over time under tVNS compared to sham (p=.017). The relative increase in HRV and decrease in HR was associated with increased oxygenation of the PFC (HR: p<.0001; HRV: p=.007). Exploratory analyses illustrated, that under tVNS, PFC connectivity increased compared to sham. The present study supports the NIM by showing that tVNS influences ANS activity and that relative changes in PFC oxygenation contribute to these effects. Implications of these findings and directions for further research are discussed.

Validity of the Polar H10 Sensor for Heart Rate Variability Analysis during Resting State and Incremental Exercise in Recreational Men and Women.

Heart rate variability (HRV) is frequently applied in sport-specific settings. The rising use of freely accessible applications for its recording requires validation processes to ensure accurate data. It is the aim of this study to compare the HRV data obtained by the Polar H10 sensor chest strap device and an electrocardiogram (ECG) with the focus on RR intervals and short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA a1) as non-linear metric of HRV analysis. A group of 25 participants performed an exhaustive cycling ramp with measurements of HRV with both recording systems. Average time between heartbeats (RR), heart rate (HR) and DFA a1 were recorded before (PRE), during, and after (POST) the exercise test. High correlations were found for the resting conditions (PRE: r = 0.95, rc = 0.95, ICC3,1 = 0.95, POST: r = 0.86, rc = 0.84, ICC3,1 = 0.85) and for the incremental exercise (r > 0.93, rc > 0.93, ICC3,1 > 0.93). While PRE and POST comparisons revealed no differences, significant bias could be found during the exercise test for all variables (p < 0.001). For RR and HR, bias and limits of agreement (LoA) in the Bland–Altman analysis were minimal (RR: bias of 0.7 to 0.4 ms with LoA of 4.3 to −2.8 ms during low intensity and 1.3 to −0.5 ms during high intensity, HR: bias of −0.1 to −0.2 ms with LoA of 0.3 to −0.5 ms during low intensity and 0.4 to −0.7 ms during high intensity). DFA a1 showed wider bias and LoAs (bias of 0.9 to 8.6% with LoA of 11.6 to −9.9% during low intensity and 58.1 to −40.9% during high intensity). Linear HRV measurements derived from the Polar H10 chest strap device show strong agreement and small bias compared with ECG recordings and can be recommended for practitioners. However, with respect to DFA a1, values in the uncorrelated range and during higher exercise intensities tend to elicit higher bias and wider LoA.

Correlation between heart rate variability and polysomnography‐derived scores of severities for obstructive sleep apnea

Obstructive sleep apnea (OSA) is one of the most common sleep disorders and affects nearly a billion people worldwide. Furthermore, it is estimated that many patients with OSA are underdiagnosed, which contributes to the development of comorbidities, such as cardiac autonomic imbalance, leading to high cardiac risk. Heart rate variability (HRV) is a non‐invasive, widely used approach to evaluate neural control of the heart. This study aims to seek the relationship between HRV indices and the presence and severity of OSA. Polysomnography (PSG) exams of 157 patients were classified into four groups: OSA‐free (N=26), mild (N=39), moderate (N=37), and severe OSA (N=55). The electrocardiogram was extracted from the PSG, and a 15‐min beat‐by‐beat series of RR intervals was generated every hour during the first 6 h of sleep. Linear and nonlinear HRV approaches were employed to calculate a variety of indices of HRV. Specifically, time‐ and frequency‐domain, symbolic analysis, entropy measures, heart rate fragmentation, acceleration and deceleration capacities, asymmetry measures, and fractal analysis (detrended fluctuation analysis). Results with indices of sympathovagal balance reinforced previous findings that patients with OSA have sympathetic overactivity. Nonlinear indices showed that these patients suffer from a loss of physiologic complexity, contributing to their higher risk of development of cardiovascular disease. Moreover, as shown in the table below, many HRV indices presented statistically significant correlation with PSG's clinical scores: apnea/hypopnea index (AHI), microarousal index (MI), percentage of the total sleep time that the patient was with oxygen saturation below 90% (T90), and oxygen saturation nadir during sleep (SatMin). Therefore, a complete set of HRV indices, especially the nonlinear ones, can bring valuable information about the presence and severity of OSA, suggesting that HRV can be helpful in a quick diagnosis of OSA, potentially reducing the development of comorbidities.

Entropy Analysis of Heart Rate Variability in Different Sleep Stages.

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

Quantification of Cardiovascular Regulation Applying Heart Rate Variability Analyses for Different Warm and Moist Chest Compresses in Healthy Subjects.

Background: In integrative medicine, complementary healing methods, such as external applications (massages, rhythmic rubs, and compresses), are part of the practice and clinical application and have proven their therapeutic effect in various fields. Objective: Aim of this exploratory, controlled, single-blinded study was to investigate the effects of three different warming chest compresses on cardiovascular regulation by analyzing heart rate variability (HRV) in healthy subjects. Methods: Over a period of 4 weeks, three different warming chest compresses (a hot water compress, a ginger powder compress, and a mustard flour compress) in 30 healthy subjects were analyzed. For all subjects, 48-h long-term electrocardiograms were recorded, and afterward, epochs of 5 min length extracted and analyzed by different linear and nonlinear HRV indices. Results: A moist chest compress did not result in any significant short- and long-term stimulation of the autonomic regulation, except for a short-term significant decrease in heart rate (meanNN, p < 0.05). Warm and moist chest compresses with ginger flour led to significantly increased HRV (sdNN, p < 0.05; symbolic dynamics2, p < 0.05) and its complexity (renyi4 entropy, p < 0.05) and a significant decrease in heart rate (meanNN, p < 0.00036), and thus to a short-term relaxation effect. In contrast, warm and moist chest compresses with mustard flour led to significantly decreased HRV and their complexity (time-, frequency-, and nonlinear dynamics domain, p < 0.00036), which can be interpreted as a stress reaction of the autonomous nervous system. Conclusions: The application of chest compresses led to short-term relaxation effects (ginger) as well as short-term stress effects (mustard) but not to any significant longer-term effect on HRV in healthy subjects.

Stress level measurements using heart rate variability analysis on android based application

This paper present stress level measurements using heart rate variability (HRV) analysis on an android based application. Stress level usually measured using a subjective methodology to extract perception of stress. We use HRV to extract the real existence of stress via observing the stress impact to the autonomic nervous system (ANS). Ten subjects participated in this study. Linear and nonlinear analysis of HRV was performed. The results shows that subjects with higher stress level have higher HRV. We use the results to build a simple classifier model for classification resulting 70% accuracy using only linear analysis HRV parameter.

Heart rate variability analysis to improve early diagnostic and prognostic differentiation of parkinsonism of uncertain etiology. A 10 years follow-up study

Abstract Introduction Parkinsonian syndromes of different nature feature autonomic nervous system dysfunction (ANSd) which may herald or follow motor symptoms (MS) in Parkinson disease (PD), but anticipate by years and progress more rapidly in multiple system atrophy (MSA). Abnormal Heart Rate Variability (HRV) has been reported in both PD and MSA. Purpose This study aimed to evaluating if HRV analysis (HRVa) could contribute to differentiate PD from MSA and to guide early and more appropriate treatment and prognostic assessment. Methods 66 consecutive parkinsonian patients (pts) (48 males, 18 females) (mean age 63.8±10 y) were enrolled between 2010 and 2020. Initial clinical diagnosis was MSA (19), PD (20), PD with autonomic impairment (PD-AI) (10), Lewy body disease (2), supranuclear palsy (3), undefined Parkinsonism (UP) (12). The severity of MS was quantified with UPDRS and Hoehn/Yahr scales. HRV was analyzed with linear (TD and FD) and non-linear (NL) methods (Kubios Premium 3.4.1), during daily activity and NREM sleep (2-minutes intervals) and during REM sleep (1-minute Interval). 44/66 underwent also ASN functional evaluation with the Ewing protocol. Data of 52 age-matched healthy subjects (HS) were used for comparison. For statistical analysis, SPSS software (version 21) was used. Discriminant Analysis (DA) was applied to identify the more relevant parameters differentiating PD from MSA and classifying “undefined” cases. Prolonged follow-up (f-up) provided clinical diagnostic certainty, as goldstandard for validation of HRVa classification accuracy. Results UPDRS and Hoehn/Yahr scores were higher in MSA than in PD (p&amp;lt;0.0001). Along 10-years of f-up, 16/66 pts died (56,2% MSA), 5/66 had new diagnostic definition, and 15/66 were lost to f-up. Most of HRV parameters of 34 pts with certain diagnosis (17 MSA and 17 PD) whose f-up was completed, were significantly different from those of HS. VLFpower and recurrence plot parameters calculated during active wakefulness were different (p&amp;lt;0.05) between MSA and PD, and correctly classified them with 88,2% predictive accuracy (PA) at DA, according to the formula: F1 = (vlfpw%ar* − 0,087) + (rprec* − 0,319) + (rpshen*10.81) + (−20,53) Comparative classification accuracy of HRVa, Ewing score, UPDRS and Hoehn/Yahr scales is shown in Table 1. Attempting prognostic risk assessment by applying F1 to HRV data of pts with PD-AI and of pts with UP, those classified as MSA according to F1 showed the worst clinical outcome during the f-up. Conclusions This study confirms that a higher degree of prevailing sympathetic ASNd in MSA compared to PD can be identified by a combination of Linear and NL HRV parameters, and correctly differentiate with high (88,2%) predictive accuracy all pts with certain diagnosis verified during prolonged f-up. Moreover, HRVa might be useful for earlier prognostic stratification of pts with parkinsonian symptoms of uncertain nature and to improve clinical and pharmacological management. Funding Acknowledgement Type of funding sources: None.