Linear Heart Rate Variability Parameters Research Articles

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.

Heart rate variability response to a mobile phone call in a Greek adult population

This pilot study aimed to evaluate the autonomic nervous system (ANS) disturbance before, during and after a cell phone call, in healthy adults. The opted ANS biomarker was heart rate variability (HRV), as it has been previously related to cardiovascular morbidity, mortality and depression. Twenty-four healthy volunteers (aged 34.5 ± 11.5y) entered this prospective study, after providing written informed consent. Their characteristics are presented in Table 1 . All were free of medication and underwent measurement of linear HRV parameters (RRI, LF, HF, LF/HF, SDNN, SDANN, NN50, RMSSD) with CNS Task Force Monitor in supine position, in the cardiovascular lab, in the same time of the day (15.30–16.30). Measurements were recorded during baseline relaxation (15 min), while speaking on their own cell phone (5 min) and after cell phone call end (20 min) ( Fig. 1 ). Alterations along the successive phases were statistically evaluated with repeated measures analysis in SPSS 17. Only mean sinus rhythm of RR intervals was significantly changed ( P < 0.01). This finding persisted during the 20 minutes of relaxation period following call. Even a 5 minute call causes a subtle but persisting response of HRV. This study may stimulate further research on cell phone use impact on healthy and patient subjects.

Heart rate variability time domain features in automated prediction of diabetes in rat.

Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system. In this work, an alternative diagnostic system based on the heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM) have been proposed. The experiment and data recording has been performed on male Wister rats of 10-12week of age and 200 ± 20gm of weight. The digital lead-I electrocardiogram (ECG) data are recorded from control (n = 5) and Streptozotocin-induced diabetic rats (n = 5). Nine time-domain linear HRV parameters are computed from 60s of ECG data epochs and used for the training and testing of backpropagation ANN and SVM. Total 526 (334 Control and 192 diabetics) such datasets are computed for the testing of ANN for the identification of the diabetic conditions. The ANN has been optimized for architecture 9:5:1 (Input: hidden: output neurons, respectively) with the optimized learning rate parameter at 0.02. With this network, a very good classification accuracy of 96.2% is achieved. While similar accuracy of 95.2% is attained using SVM. Owing to the successful implementation of HRV parameters based automated classifiers for diabetic conditions, a non-invasive, ECG based online prognostic system can be developed for accurate and non-invasive prediction of the diabetic condition.

Usefulness of heart rhythm complexity in heart failure detection and diagnosis

Heart failure (HF) is a major cardiovascular disease worldwide, and the early detection and diagnosis remain challenges. Recently, heart rhythm complexity analysis, derived from non-linear heart rate variability (HRV) analysis, has been proposed as a non-invasive method to detect diseases and predict outcomes. In this study, we aimed to investigate the diagnostic value of heart rhythm complexity in HF patients. We prospectively analyzed 55 patients with symptomatic HF with impaired left ventricular ejection fraction and 97 participants without HF symptoms and normal LVEF as controls. Traditional linear HRV parameters and heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) were analyzed. The traditional linear HRV, MSE parameters and DFAα1 were significantly lower in HF patients compared with controls. In regression analysis, DFAα1 and MSE scale 5 remained significant predictors after adjusting for multiple clinical variables. Among all HRV parameters, MSE scale 5 had the greatest power to differentiate the HF patients from the controls in receiver operating characteristic curve analysis (area under the curve: 0.844). In conclusion, heart rhythm complexity appears to be a promising tool for the detection and diagnosis of HF.

S214. HEART RATE VARIABILITY PREDICTS CLOZAPINE PLASMA LEVEL IN PATIENTS WITH SCHIZOPHRENIA IN A POLYPHARMACY CONDITION

BackgroundClozapine (CLZ) is most specific suitable treatment of refractory schizophrenia; however, it might lead a variety of adverse reactions that cause drug non-compliance. Although it is necessary to conduct therapeutic drug monitoring through measuring psychoactive drug plasma concentration measurement, it has limitation in that it takes several days and it is invasive. Previous studies have suggested that the heart rate variability (HRV) might serves as non-invasive and fast marker to monitoring pharmacodynamic intra-individual effects of the drug. This study aimed to investigate whether and which HRV parameters predict CLZ plasma concentration in natural clinical and polypharmacy conditions.MethodsTotal 85 patients (Male = 49 and Female = 36; Mean age = 35.95±9.45 years old; Mean CLZ intake = 6.21±4.86 years old) with schizophrenia who had received clozapine without the change of clozapine dosage at least 7 days before blood sampling for TDM and electrocardiogram (ECG) measuring participated in this study. ECG data were collected during resting-state. Linear and nonlinear HRV analysis were ran.ResultsBased on significant results from correlational analysis with the CLZ plasma level, univerate generalized linear regression model (GLM) with gamma distribution were conducted with adjusting the effect of age and sex. As results, for linear parameters of HRV, pNN5, pNN10, and VLF were inversely associated with the CLZ plasma level (Ps<0.05). For nonlinear HRV, DFA2 and crossover were positively and CSE30 were inversely associated with the CLZ concentration (Ps<0.05). After that, step-wise multiple GLM was conducted and the result yield that pNN5 and crossover are best predictors of CLZ plasma level (β=-0.01, t=-3.85, P<0.001 and β=0.50, t=5.02, P<0.001, respectively). The final model with pNN5 and predicted 35.78% of CLZ concentration. Among dependent variables in this model, crossover were positively correlated with CLZ dose (r=0.48, P<0.001).DiscussionHRV, especially for crossover, could be a candidate of a surrogate marker of CLZ concentration for drug-monitoring in patients with treatment-resistant schizophrenia in a polypharmacy condition. Findings of this study would helpful to make a clinical decision of CLZ dosage adjustment on site without the delay.

Time-domain HRV Analysis of ECG Signal under Different Body Postures

Heart rate variability (HRV) is a cardiac estimate derived from the ECG signal. The analysis of HRV offers techniques of evaluating input into the cardiac rhythm non-invasively. For the present work, data of ten participants in three different body postures were taken. Sets of data were acquired in sitting, standing and sleeping positions. ECG derived R-peak is used for assessment of RR interval, which is further utilized for analysis of HRV. HRV is linked with mean heart rate (HR) namely tachycardia having HR>100bpm and bradycardia having HR<60bpm. Linear HRV parameters having different time-domain indices like HR, RR-interval, RMSSD, SDNN, NN50, pNN50 and frequency-domain indices such as LFnu, HFnu, LF/HF are interpreted in three different body postures.From the result, it can be concluded that in all three postures, the RR interval becomes larger for supine posture followed by sitting and standing as supine seems to be a more relaxed condition than the other two. Also, the frequency-domain analysis result suggests, supine posture has a higher LF/HF ratio i.e. higher sympathetic influence and hence supine has a better resting condition than sitting and standing.

Heart Rhythm Complexity Impairment in Patients with Pulmonary Hypertension

Pulmonary hypertension is a fatal disease, however reliable prognostic tools are lacking. Heart rhythm complexity analysis is derived from non-linear heart rate variability (HRV) analysis and has shown excellent performance in predicting clinical outcomes in several cardiovascular diseases. However, heart rhythm complexity has not previously been studied in pulmonary hypertension patients. We prospectively analyzed 57 patients with pulmonary hypertension (31 with pulmonary arterial hypertension and 26 with chronic thromboembolic pulmonary hypertension) and compared them to 57 age- and sex-matched control subjects. Heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) and linear HRV parameters were analyzed. The patients with pulmonary hypertension had significantly lower mean RR, SDRR, pNN20, VLF, LF, LF/HF ratio, DFAα1, MSE slope 5, scale 5, area 1–5 and area 6–20 compared to the controls. Receiver operating characteristic curve analysis showed that heart rhythm complexity parameters were better than traditional HRV parameters to predict pulmonary hypertension. Among all parameters, scale 5 had the greatest power to differentiate the pulmonary hypertension patients from controls (AUC: 0.845, P < 0.001). Furthermore, adding heart rhythm complexity parameters significantly improved the discriminatory power of the traditional HRV parameters in both net reclassification improvement and integrated discrimination improvement models. In conclusion, the patients with pulmonary hypertension had worse heart rhythm complexity. MSE parameters, especially scale 5, had excellent single discriminatory power to predict whether or not patients had pulmonary hypertension.

Analysis of Linear and Non-linear parameters of HRV for opting optimum parameters in wearable device

The Human heart rate though appears regular, it is not. There is a change in the time intervals between two successive heart beats, these variations are referred to as heart rate variability (HRV). The HRV will be in certain regular pattern and differs from individuals. HRV depends on state of pathology and physiology of the individual. Research has been carried out to find whether if they could predict the disease early and could avoid complications which might be fatal. It helps the physician in early diagnosis and alertness of the disease which will denote the step for better care. The vital advantage of HRV is that, it is noninvasive and the individuals can check there HRV parameters without any inconvenience at any time without any after effects. In this work ECG signals are obtained from physionet data base. Data of normal ECG signals and ECG of Supraventricular Tachycardia of 57 subjects each are considered for the study of HRV parameters. Linear parameters of HRV like time-domain, frequency-domain parameters non-linear parameters like Poincare plot is analyzed. The new software is developed on MATLAB tool for carrying HRV analysis, code has been developed to obtain linear and nonlinear parameters of HRV with the filtering option embedded in the software. Using the developed software statistical analyses was carried on to obtain the optimum parameter that can distinguish normal and diseased conditions which can be further considered for embedding in wearable device hardware. Though HRV has huge potential in diagnosing and predicting diseases, it is yet to be used as a diagnostic tool. Identification of Linear and non-linear parameters which are significant in diagnoses of diseases will be useful in predicting ANS modifying diseases early.

Validation of a New Heart Rate Measurement Algorithm for Fingertip Recording of Video Signals with Smartphones.

This study investigates the accuracy of a heart rate (HR) measurement algorithm applied to a pulse wave. This was based on video signals recorded with a smartphone. The results of electrocardiographic HR and standard linear heart rate variability (HRV) analysis were used for reference. On a total of 68 subjects, an electrocardiogram (ECG) and the pulse curve were simultaneously recorded on an Apple iPhone 4S. The HR was measured using an algorithm developed by the authors that works according to a method combining the detection of the steepest slope of every pulse wave with the correlation to an optimized pulse wave pattern. The results of the HR measured by pulse curves were extremely consistent (R > 0.99) with the HR measured on ECGs. For most standard linear HRV parameters as well, high correlations of R ≥ 0.90 in the analysis were achieved in the time and frequency domain. In conclusion, the overall accuracy of HR and HRV indices of pulse wave analysis, based on video signals of a smartphone, with the developed algorithm was sufficient for preclinical screening applications.

Detrended Fluctuation Analysis of Heart Rate Dynamics Is an Important Prognostic Factor in Patients with End-Stage Renal Disease Receiving Peritoneal Dialysis.

Background and ObjectivesPatients with severe kidney function impairment often have autonomic dysfunction, which could be evaluated noninvasively by heart rate variability (HRV) analysis. Nonlinear HRV parameters such as detrended fluctuation analysis (DFA) has been demonstrated to be an important outcome predictor in patients with cardiovascular diseases. Whether cardiac autonomic dysfunction measured by DFA is also a useful prognostic factor in patients with end-stage renal disease (ESRD) receiving peritoneal dialysis (PD) remains unclear. The purpose of the present study was designed to test the hypothesis.Materials and MethodsPatients with ESRD receiving PD were included for the study. Twenty-four hour Holter monitor was obtained from each patient together with other important traditional prognostic makers such as underlying diseases, left ventricular ejection fraction (LVEF) and serum biochemistry profiles. Short-term (DFAα1) and long-term (DFAα2) DFA as well as other linear HRV parameters were calculated.ResultsA total of 132 patients (62 men, 72 women) with a mean age of 53.7±12.5 years were recruited from July 2007 to March 2009. During a median follow-up period of around 34 months, eight cardiac and six non-cardiac deaths were observed. Competing risk analysis demonstrated that decreased DFAα1 was a strong prognostic predictor for increased cardiac and total mortality. ROC analysis showed that the AUC of DFAα1 (<0.95) to predict mortality was 0.761 (95% confidence interval (CI). = 0.617–0.905). DFAα1≧ 0.95 was associated with lower cardiac mortality (Hazard ratio (HR) 0.062, 95% CI = 0.007–0.571, P = 0.014) and total mortality (HR = 0.109, 95% CI = 0.033–0.362, P = 0.0003).ConclusionCardiac autonomic dysfunction evaluated by DFAα1 is an independent predictor for cardiac and total mortality in patients with ESRD receiving PD.

Assessment of fetal maturation age by heart rate variability measures using random forest methodology

Fetal maturation age assessment based on heart rate variability (HRV) is a predestinated tool in prenatal diagnosis. To date, almost linear maturation characteristic curves are used in univariate and multivariate models. Models using complex multivariate maturation characteristic curves are pending.To address this problem, we use Random Forest (RF) to assess fetal maturation age and compare RF with linear, multivariate age regression. We include previously developed HRV indices such as traditional time and frequency domain indices and complexity indices of multiple scales.We found that fetal maturation was best assessed by complexity indices of short scales and skewness in state-dependent datasets (quiet sleep, active sleep) as well as in state-independent recordings. Additionally, increasing fluctuation amplitude contributed to the model in the active sleep state. None of the traditional linear HRV parameters contributed to the RF models. Compared to linear, multivariate regression, the mean prediction of gestational age (GA) is more accurate with RF than in linear, multivariate regression (quiet state: R2=0,617 vs. R2=0,461, active state: R2=0,521 vs. R2=0,436, state independent: R2=0,583 vs. R2=0,548).We conclude that classification and regression tree models such as RF methodology are appropriate for the evaluation of fetal maturation age. The decisive role of adjustments between different time scales of complexity may essentially extend previous analysis concepts mainly based on rhythms and univariate complexity indices. Those system characteristics may have implication for better understanding and accessibility of the maturating complex autonomic control and its disturbance.

Effect of ECG Sampling Frequency on Approximate Entropy based HRV

Heart rate variability (HRV) analysis, instantaneous variation in RR intervals time series of electrocardiogram (ECG), is generally used for evaluating autonomic nervous system (ANS) functioning in cardiovascular research and in different human well being related applications. Approximate entropy (ApEn) is a nonlinear metric used to measure the irregularity of a RR interval time series. An increase in ApEn is often associated to increases in complexity. Linear HRV parameters are very sensitive to ECG sampling frequency and low sampling frequency may result in clinically misinterpretation of HRV. In this study consequences of errors in ApEn based HRV induced by ECG sampling frequency have been investigated. Error in ApEn measure of HRV was found to be a function of data length of RR interval time series and ECG sampling frequency. The percentage difference in ApEn was more than 3.5%.for long term data (N=1000), more than 2.5% for medium data (N=500) and less than 1% for short term data (N=200) at low ECG sampling frequency of 125 Hz with respect to reference values at 2000 Hz. Thus, the results of indices such as ApEn when applied to time series with low ECG sampling should be regarded with caution.

Heart rate variability and phantom pain in male amputees: Application of linear and nonlinear methods

Phantom-limb pain (PLP) is a phenomenon that may appear among people with amputation. Some studies reveal that 70% of people with amputation experience PLP years postamputation. There is a lack of scientific evidence about the cause of PLP. It has been hypothesized that the autonomic nervous system (ANS) could be involved in the mechanism that triggers PLP, but this hypothesis remains unclear. The aim of this study was to correlate ANS function, through heart rate variability (HRV) analysis, with PLP in adult males with amputation. The study population comprised 35 subjects, with 27 reporting PLP often or always. The rest of the subjects did not report any PLP. In order to calculate linear and nonlinear parameters of HRV, all subjects underwent 10 min of resting heart rate monitoring. The study did not find correlations between HRV parameters and PLP. Most of the subjects showed decreased values in linear parameters of HRV while nonlinear values were normal. HRV is not implicated in PLP. Linear and nonlinear methods for HRV analysis might reflect different physiological phenomena; while linear values place people with amputation at cardiovascular risk, nonlinear values indicate normality.

Heart rate variability during REM and non-REM sleep in preterm neonates with and without abnormal cardiorespiratory events

Aim Analyse heart rate variability (HRV) of preterm neonates undergoing a polysomnography in relation to the occurrence of abnormal cardiorespiratory events on one hand and the type of sleep states on the other hand. Methods To quantify nonlinear HRV, the numerical noise titration technique is used, adapted to neonatal heart rate data. HRV is calculated for 30 preterm neonates with mean post-conceptional age of 36.4 weeks, divided into three groups according to the occurrence of abnormal events during the polysomnographies and the eventual home monitoring. Results Periods of non-REM sleep have lower noise limit values and can be distinguished significantly from periods of REM sleep and from the total recording period. The presence of abnormal events does not influence this finding. Significant differences between groups are only found during non-REM segments by means of the noise limit value computed via numerical noise titration while the linear HRV parameters were not able to discriminate. Conclusion ECG measurement of a relatively short non-REM sleep period without specific abnormal events is sufficient to define a mature cardiorespiratory pattern in preterm infants.

Recurrences in heart rate dynamics are changed in patients with diabetes mellitus

Detection of subclinical autonomic dysfunction in patients with diabetes mellitus (DM) is of vital importance for risk stratification and subsequent management. Heart rate variability (HRV) analysis is a sensitive tool for assessment of cardiovascular autonomic dysfunction. As the heart is controlled by non-linear deterministic system, the non-linear dynamics measures should be preferred. Recurrence plot (RP) is able to analyse recurrences within system dynamics. The aim of the study was to detect heart rate dysregulation in DM by RP and to ascertain which of the recurrence quantification analysis (RQA) measures are changed in patients with DM compared to control group. We analysed HRV recordings from 17 young patients with type 1 DM and 17 healthy matched control subjects. RQA was performed on RPs with a fixed value of recurrence points percentage. From RQA measures based on diagonal lines, we have found higher percentage of determinism in DM group (P=0.038). Trapping time measure was also higher in DM (P=0.022). RQA revealed changes in dynamics recurrences with reduced complexity of heart rate control in young diabetic patients. As RQA parameters are independent of overall HRV, parameters of RP should be used together with linear HRV parameters for better description of heart rate dysregulation in patients with diabetics.

Heart rate variability: A noninvasive approach to measure stress in calves and cows

The aim of this study was to evaluate the usefulness of heart rate variability (HRV) and its specific parameters as a new approach to assess stress load in cattle. We recorded HRV in 52 calves in three groups and in 31 cows in two groups. In calves we divided Group 1 with no obvious stress load ( n=18), Group 2 with external stress load ( n=17), and Group 3 with internal stress load from sickness ( n=17). In cattle we divided lactating cows ( n=21) and nonlactating cows ( n=10). HRV parameters were analyzed in the time domain and in the frequency domain. Moreover, we applied Recurrence Quantification Analysis (RQA) to quantify nonlinear components of HRV. In calves, linear HRV parameter decreased from Group 1 to Group 3 ( P<.05). However, not a single parameter showed significant differences regarding all three groups. The value of all nonlinear measurements increased at the same time ( P<.05). The only parameter that exhibited significant differences between all three groups was the longest diagonal line segment in the recurrence plot ( L MAX) which is inversely related to the Lyapunov exponent. We did not find differences concerning the linear HRV parameters between the two groups in the cows. The nonlinear parameter Determinism showed significant higher values in lactating cows compared to nonlactating cows. The importance of particular HRV-parameters was tested by applying a discriminant analysis approach. In calves and cattle nonlinear parameters were most important to indicate the level of stress load on the animals. Based on the results we assume HRV to be a valuable physiological indicator for stress load in animals. Whereas linear parameters of HRV are supposed to be useful to separate qualitative different level of stress, nonlinear components of HRV distinguish quantitative different challenges for the animals.

Behavioral and EEG measures of sleeping and waking in the preterm period

Analyse heart rate variability (HRV) of preterm neonates undergoing a polysomnography in relation to the occurrence of abnormal cardiorespiratory events on one hand and the type of sleep states on the other hand.To quantify nonlinear HRV, the numerical noise titration technique is used, adapted to neonatal heart rate data. HRV is calculated for 30 preterm neonates with mean post-conceptional age of 36.4 weeks, divided into three groups according to the occurrence of abnormal events during the polysomnographies and the eventual home monitoring.Periods of non-REM sleep have lower noise limit values and can be distinguished significantly from periods of REM sleep and from the total recording period. The presence of abnormal events does not influence this finding. Significant differences between groups are only found during non-REM segments by means of the noise limit value computed via numerical noise titration while the linear HRV parameters were not able to discriminate.ECG measurement of a relatively short non-REM sleep period without specific abnormal events is sufficient to define a mature cardiorespiratory pattern in preterm infants.