Nonlinear Analysis Of Heart Rate Variability Research Articles

Biofeedback of elderly patients with chronic pain: new nonlinear Heart Rate Variability analysis

Biofeedback of elderly patients with chronic pain: new nonlinear Heart Rate Variability analysis

Predominant cardiac sympathetic modulation during wake and sleep in patients with Rett syndrome

BackgroundRett syndrome (RTT) is a rare neurological disorder primarily associated with mutations in the methyl-CpG-binding protein 2 (MECP2) gene. The syndrome is characterized by cognitive, social, and physical impairments, as well as sleep disorders and epilepsy. Notably, dysfunction of the autonomic nervous system is a key feature of the syndrome. Although Heart Rate Variability (HRV) has been used to investigate autonomic nervous system dysfunction in RTT during wakefulness, there is still a significant lack of information regarding the same during sleep. Therefore, our aim was to investigate cardiovascular autonomic modulation during sleep in subjects with RTT compared to an age-matched healthy control group (HC). MethodA complete overnight polysomnographic (PSG) recording was obtained from 11 patients with Rett syndrome (all females, 10 ± 4 years old) and 11 HC (all females, 11 ± 4 years old; p = 0.48). Electrocardiogram and breathing data were extracted from PSG and divided into wake, non-REM, and REM sleep stages. Cardiac autonomic control was assessed using symbolic non-linear heart rate variability analysis. The symbolic analysis identified three patterns: 0 V% (sympathetic), 2UV%, and 2LV% (vagal). ResultsThe 0 V% was higher in the RTT group than in the HC group during wake, non-REM, and REM stages (p < 0.01), while the 2LV and 2UV% were lower during wake and sleep stages (p < 0.01). However, the 0 V% increased similarly from the wake to the REM stage in both RTT and HC groups. ConclusionsTherefore, the sympatho-vagal balance shifted towards sympathetic predominance and vagal withdrawal during wake and sleep in RTT, although cardiac autonomic dynamics were preserved during sleep.

Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch.

Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics. Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable. Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages. Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user's or clinic's bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.

The influence of combined effects of hyperbaria and hyperoxia on human heart rate variability during exercise strain

With the participation of 8 male volunteers, the dynamics of values of heart rate autonomic regulation were studied at response to physical load on a bicycle ergometer twice: both respirating by the room air under normobaric environment and simulating effects of the altered hyperoxic respiratory gas mixture (HRGM) by the rebreathe respiration methods from a rebreather at the high-pressure chamber (at «depth» 7 m). The devices and methods of Holter electrocardiogram monitoring were used under 24 hours research. The specialized software to analyze the data was used. The electrocardiogram (ECG) and calculated time series of cardiac intervals data obtained were subjected to mathematical processing to calculate statistical and spectral values of heart rate variability (HRV). Additionally, the geometric and nonlinear analysis methods of HRV were investigated. It has been established that influencing factors (compound and pressure of the HRGM) affect the autonomic regulation of heartbeats both at rest and during physical load. Clinical practitioners the HRV values are studied at rest and during strictly regulated tests, i.e. in stationary conditions. The analysis of non-stationary processes, for example, when it is necessary to continuously analyze data, the so-called transient processes under increasing psychological stress, to assess changes in the autonomic regulation of the heart during breathing of gas mixtures with different compositions (hypoxic therapy, hyperbaric oxygenation, etc.), and, especially during physically upload are used the methods of nonlinear and geometric analysis of HRV that are independent of the degree of nonstationary processes and, to some extent, of artifactual cardiac intervals. During the breathing the respiratory gas mixtures with an increased substance of O2 under conditions of high pressure, the activity of the parasympathetic branches of the autonomic nervous system (ANS) significantly increases. The nonlinear dynamics indexes reflect significant differences in the activity of autonomic components under experimental conditions in the direction of strengthening the parasympathetic links of the ANS. The use of nonlinear analysis values is more acceptable when evaluating obtained of non-stationary processes. In general, the study confirms that the regulatory response to the effects of hyperbaria and hyperoxia when performing a bicycle ergometer test with stepwise increase exercise strain is manifested by the prevalence of the activity of the parasympathetic part of the ANS autonomic regulation of cardiac rhythm compared to regular room air and barometric pressure.

Pathological Heart Rate Regulation in Apparently Healthy Individuals.

Cardiovascular diseases are the leading cause of morbidity and mortality in adults worldwide. There is one common pathophysiological aspect present in all cardiovascular diseases-dysfunctional heart rhythm regulation. Taking this aspect into consideration for cardiovascular risk predictions opens important research perspectives, allowing for the development of preventive treatment techniques. The aim of this study was to find out whether certain pathologically appearing signs in the heart rate variability (HRV) of an apparently healthy person, even with high HRV, can be defined as biomarkers for a disturbed cardiac regulation and whether this can be treated preventively by a drug-free method. This multi-phase study included 218 healthy subjects of either sex, who consecutively visited the physician at Gesundheit clinic because of arterial hypertension, depression, headache, psycho-emotional stress, extreme weakness, disturbed night sleep, heart palpitations, or chest pain. In study phase A, baseline measurement to identify individuals with cardiovascular risks was done. Therefore, standard HRV, as well as the new cardiorhythmogram (CRG) method, were applied to all subjects. The new CRG analysis used here is based on the recently introduced LF drops and HF counter-regulation. Regarding the mechanisms of why these appear in a steady-state cardiorhythmmogram, they represent non-linear event-based dynamical HRV biomarkers. The next phase of the study, phase B, tested whether the pathologically appearing signs identified via CRG in phase A could be clinically influenced by drug-free treatment. In order to validate the new CRG method, it was supported by non-linear HRV analysis in both phase A and in phase B. Out of 218 subjects, the pathologically appearing signs could be detected in 130 cases (60%), p < 0.01, by the new CRG method, and by the standard HRV analysis in 40 cases (18%), p < 0.05. Thus, the CRG method was able to detect 42% more cases with pathologically appearing cardiac regulation. In addition, the comparative CRG analysis before and after treatment showed that the pathologically appearing signs could be clinically influenced without the use of medication. After treatment, the risk group decreased eight-fold-from 130 people to 16 (p < 0.01). Therefore, progression of the detected pathological signs to structural cardiac pathology or arrhythmia could be prevented in most of the cases. However, in the remaining risk group of 16 apparently healthy subjects, 8 people died due to all-cause mortality. In contrast, no other subject in this study has died so far. The non-linear parameter which is able to quantify the changes in CRGs before versus after treatment is FWRENYI4 (symbolic dynamic feature); it decreased from 2.85 to 2.53 (p < 0.001). In summary, signs of pathological cardiac regulation can be identified by the CRG analysis of apparently healthy subjects in the early stages of development of cardiac pathology. Thus, our method offers a sensitive biomarker for cardiovascular risks. The latter can be influenced by non-drug treatments (acupuncture) to stop the progression into structural cardiac pathologies or arrhythmias in most but not all of the patients. Therefore, this could be a real and easy-to-use supplemental method, contributing to primary prevention in cardiology.

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.

Evaluation of the Methods for Nonlinear Analysis of Heart Rate Variability

The dynamics of cardiac signals can be studied using methods for nonlinear analysis of heart rate variability (HRV). The methods that are used in the article to investigate the fractal, multifractal and informational characteristics of the intervals between heartbeats (RR time intervals) are: Rescaled Range, Detrended Fluctuation Analysis, Multifractal Detrended Fluctuation Analysis, Poincaré plot, Approximate Entropy and Sample Entropy. Two groups of people were studied: 25 healthy subjects (15 men, 10 women, mean age: 56.3 years) and 25 patients with arrhythmia (13 men, 12 women, mean age: 58.7 years). The results of the application of the methods for nonlinear analysis of HRV in the two groups of people studied are shown as mean ± std. The effectiveness of the methods was evaluated by t-test and the parameter Area Under the Curve (AUC) from the Receiver Operator Curve (ROC) characteristics. The studied 11 parameters have statistical significance (p &lt; 0.05); therefore, they can be used to distinguish between healthy and unhealthy subjects. It was established by applying the ROC analysis that the parameters Hq=2(MFDFA), F(α)(MFDFA) and SD2(Poincaré plot) have a good diagnostic value; H(R/S), α1(DFA), SD1/SD2(Poincaré plot), ApEn and SampEn have a very good score; α2(DFA), αall(DFA) and SD1(Poincaré plot) have an excellent diagnostic score. In conclusion, the methods used for nonlinear analysis of HRV have been evaluated as effective, and with their help, new perspectives are opened in the diagnosis of cardiovascular diseases.

When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures

Disturbed heart dynamics in depression seriously increases mortality risk. Heart rate variability (HRV) is a rich source of information for studying this dynamics. This paper is a meta-analytic review with methodological commentary of the application of nonlinear analysis of HRV and its possibility to address cardiovascular diseases in depression. This paper aimed to appeal for the introduction of cardiological screening to patients with depression, because it is still far from established practice. The other (main) objective of the paper was to show that nonlinear methods in HRV analysis give better results than standard ones. We systematically searched on the web for papers on nonlinear analyses of HRV in depression, in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 framework recommendations. We scrutinized the chosen publications and performed random-effects meta-analysis, using the esci module in jamovi software where standardized effect sizes (ESs) are corrected to yield the proof of the practical utility of their results. In all, 26 publications on the connection of nonlinear HRV measures and depression meeting our inclusion criteria were selected, examining a total of 1537 patients diagnosed with depression and 1041 healthy controls (N=2578). The overall ES (unbiased) was 1.03 (95% CI 0.703-1.35; diamond ratio 3.60). We performed 3 more meta-analytic comparisons, demonstrating the overall effectiveness of 3 groups of nonlinear analysis: detrended fluctuation analysis (overall ES 0.364, 95% CI 0.237-0.491), entropy-based measures (overall ES 1.05, 95% CI 0.572-1.52), and all other nonlinear measures (overall ES 0.702, 95% CI 0.422-0.982). The effectiveness of the applied methods of electrocardiogram analysis was compared and discussed in the light of detection and prevention of depression-related cardiovascular risk. We compared the ESs of nonlinear and conventional time and spectral methods (found in the literature) and demonstrated that those of the former are larger, which recommends their use for the early screening of cardiovascular abnormalities in patients with depression to prevent possible deleterious events.

Increased sympathetic modulation in breast cancer survivors determined by measurement of heart rate variability

Experimental and clinical studies have shown that the sympathetic nervous system (SNS) stimulates cancer progression and reduces the efficacy of oncological treatment. These effects may be reduced by pharmacological and psychotherapeutical approaches attenuating SNS tone. Therefore, it is necessary to identify those cancer survivors whose sympathetic modulation is excessively increased. For determination of SNS modulation, non-invasive method of heart rate variability (HRV) is widely used. In our study, HRV was determined from 5-min heartbeat recordings in healthy volunteers and in women with benign or malignant breast neoplasias, both in newly diagnosed patients and in women after initial treatment. We showed impaired cardio-vagal regulation in breast cancer patients (linear methods) and also found the increased sympathetic modulation indicated by the non-linear (the symbolic dynamics 0V%) parameter. This non-linear HRV analysis seems to be more sensitive than the linear one, indicating significant differences also in survivors after initial therapy in comparison to healthy controls. The lower sample entropy revealed reduced complexity in heart rate control in both breast cancer survivors groups. These findings suggest that HRV detection represents an inexpensive, easy, and reliable method for identification of those patients with breast cancer whose sympathetic modulation is significantly increased and in which the interventions, aimed at normalizing the balance in the autonomic nervous system (e.g. psychotherapy, biofeedback, treatment by β-blockers) may be the most effective.

Methods of Nonlinear Dynamics for Heart Rate Variability Analysis

The heart rate variability (HRV) analysis, based on the methods of nonlinear dynamics, can provide important information for the physiological interpretation of the functioning of the cardiovascular system and assess the risk of its pathology. The article presents methods for nonlinear analysis of HRV, united in the following groups: fractal, multifractal, graphical and informational. The application of the methods of nonlinear dynamics in the study of the information characteristics of HRV in order to distinguish healthy subjects from sick ones is an important topic from the point of view of the application of the information technologies in the field of non-invasive cardiology. After determining the values of the studied parameters with the developed software and for the distinction of the two studied groups of subjects (healthy controls and patients with arrhythmia) statistical analysis was applied. The statistical analysis was performed by t-test and receiver operating characteristic (ROC) analysis. ROC curves are constructed and the area under the curves is calculated, on the basis of which the quality of the studied methods is evaluated. The results reported in this study may be useful in classifying the states of electrocardiographic signals and serve as a landmark for comparing healthy individuals to individuals with cardiovascular disease. The high information content of the used nonlinear methods for HRV analysis opens perspectives for their future use in the diagnosis and prognosis of cardiovascular diseases.

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.

A Python Package for Heart Rate Variability Analysis and Signal Preprocessing

This paper presents <em>hrv-analysis</em>, a Python package for Heart Rate Variability (HRV) analysis. <em>hrv-analysis</em> is an open-source package for the Python statistical computing environment, which supports a wide variety of time, frequency and non-linear HRV analysis methods. It also includes several functions for signal preprocessing like outliers and ectopic beat removal. This package is suitable for researchers, professionals working in healthcare as well as for developers and more widely for anyone willing to perform detailed analysis on heart rate variability. <em>hrv-analysis</em> was developed as part of the Aura project. Aura is a non-profit project aiming to develop an open-source forecasting system to help people suffering from epilepsy regain autonomy in their everyday lives. This package was developed using Python 3.

Correlation Properties Of HRV During Marathon Racing In Recreational Runners: Potential Biomarker Of Organismic Demands

PURPOSE: Scant information exits regarding cardiovascular response and regulation in real-world endurance training/competition. Although there is data describing behavior of heart rate (HR) and HR variability (HRV) during actual events, the specific linear indexes studied have been less than optimal for assessment of real-time exercise intensity due to a lack of appreciable changes during exercise. In this context, non-linear HRV analysis is a promising approach for internal load monitoring of organismic demands related to external load markers such as intensity, cadence and duration., leading to insights in complex regulation during endurance exercise. The present study is the first to examine the influence of a marathon race on non-linear dynamics of HRV to assess its feasibility as a potential biomarker during endurance exercise. METHODS: Eleven male recreational runners performed a marathon race on an almost flat profile. During the race, HR time series was recorded continuously. HRV time-domain measurements and correlation properties using short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) were calculated. RESULTS: All runners achieved a mean total time of 3:10:22 ± 0:17:56 h:min:sec with a blood lactate concentration of 4.04 ± 1.12 mmol/l after finishing the race. Comparing the beginning to the end segment of the marathon race, significant increases could be found for km split time (p < .001, d = .934) and for HR (p = .010, d = .804). Significant decreases could be found for meanRR (p = .013, d = .798) and DFA-alpha1 (p = .003, d = 1.132). CONCLUSIONS: DFA-alpha1 showed wide dynamic range throughout the race consisting of both uncorrelated and anti-correlated values. Lactate was consistent with sustained high intensity exercise when measured at the end of the event. Despite the gradual slowing of km split times especially past the midpoint, DFA-alpha1 continued to fall to values seen in the highest intensity domain during incremental exercise testing in agreement with lactate assessment. Therefore, the discrepancy between the reduced running pace with that of the decline of DFA-alpha1, demonstrate the benefit of using this dimensionless HRV index as a potential biomarker of internal load during endurance exercise.

B-PO02-153 FLUCTUATIONS OF HEART RATE COMPLEXITY 5-MINUTE BEFORE AND AFTER PAROXYSMAL ATRIAL FIBRILLATION EVENTS: INSIGHT FROM NONLINEAR HEART RATE VARIABILITY

Autonomic modulation has been reported to related to the onset and termination of paroxysmal AF (PAF) from the linear heart rate variability (HRV). It has never been reported using the nonlinear HRV before & after the PAF events. We aimed to evaluate the changes of heart rate complexity through the nonlinear HRV analysis before & after the PAF events. We enrolled 65 patients (72 ± 12.34 years old, 31 females) with 99 PAF events who received 24-hour Holter recording. The nonlinear HRV variables, including the sample entropy (SampEn), the multi-scale entropy (MSE), and the Poincaré Plot analysis, were analyzed. HRV analyses were applied to a 20-minute window before & after the PAF events. HRV variables were evaluated based on the 5-minute time segments as we divided the 20-minute window into 8 segments. HRV variables SampEn and MSE1∼5 significantly decreased before the onset of PAF events, whereas SampEn, MSE1∼5, MSE6∼20 elevated after the termination of PAF events. SD1 and SD2, which are generated from the Poincaré Plot analysis, did not have significant changes before the PAF events. SD1 and SD2 decreased significantly after the PAF termination. Detailed serial changes of HRV variables before & after the PAF events are shown in the Table 1. In our study, the heart rate complexity, as assessed by SampEn and MSE1∼5, significantly decreased before the onset and increased after the termination of PAF. SD1, which is a marker highly related to vagal tone activity, decreased after PAF termination. These findings indicate the fluctuations of heart rate complexity and the withdrawal of vagal activities are important in the initiation and termination of PAF.

Assessment of Autonomic Nervous System Dysfunction in the Early Phase of Infection With SARS-CoV-2 Virus.

BackgroundWe are facing the outburst of coronavirus disease 2019 (COVID-19) defined as a serious, multisystem, disorder, including various neurological manifestations in its presentation. So far, autonomic dysfunction (AD) has not been reported in patients with COVID-19 infection.AimAssessment of AD in the early phase of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus).Patients and methodsWe analyzed 116 PCR positive COVID-19 patients. After the exclusion of 41 patients with associate diseases (CADG), partitioned to patients with diabetes mellitus, hypertension, and syncope, the remaining patients were included into a severe group (45 patients with confirmed interstitial pneumonia) and mild group (30 patients). Basic cardiovascular autonomic reflex tests (CART) were performed, followed by beat-to-beat heart rate variability (HRV) and systolic and diastolic blood pressure variability (BPV) analysis, along with baroreceptor sensitivity (BRS). Non-linear analysis of HRV was provided by Poincare Plot. Results were compared to 77 sex and age-matched controls.ResultsAD (sympathetic, parasympathetic, or both) in our study has been revealed in 51.5% of severe, 78.0% of mild COVID-19 patients, and the difference compared to healthy controls was significant (p = 0.018). Orthostatic hypotension has been established in 33.0% COVID-19 patients compared to 2.6% controls (p = 0.001). Most of the spectral parameters of HRV and BPV confirmed AD, most prominent in the severe COVID-19 group. BRS was significantly lower in all patients (severe, mild, CADG), indicating significant sudden cardiac death risk.ConclusionCardiovascular autonomic neuropathy should be taken into account in COVID-19 patients’ assessment. It can be an explanation for a variety of registered manifestations, enabling a comprehensive diagnostic approach and further treatment.

The Value of Heart Rhythm Complexity in Identifying High-Risk Pulmonary Hypertension Patients.

Pulmonary hypertension (PH) is a fatal disease—even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A; defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6–20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

Heart Rate Variability is Altered in College‐Aged Chronic Cannabis Users versus Non‐Users

Cannabis is an increasingly popular recreational drug among college students. Prior investigations have shown that acute cannabis use alters heart rate variability via increased sympathetic activation. However, the effects of chronic cannabis use on heart rate variability (HRV) have not been reported. PURPOSE We tested the hypothesis that HRV would be lower in chronic cannabis users (THC) compared to non-users (CON). METHODS Eleven cannabis users (age: 21±2 y, 2 females) and six controls (age: 20±2 y; 4 females) completed one study visit. Participants in the THC group self-reported using cannabis at least once per week for the past year or more. Participants in the CON reported to not use for 6 months prior to enrollment. Participants were instructed to abstain from alcohol for 24 h and cannabis for 72 h prior to the study visit. After 10 minutes of supine rest, heart rate (5-lead electrocardiogram) was continuously recorded at 1 kHz for 5 minutes of supine rest while participants breathed spontaneously. HRV was assessed using linear analysis via Fast Fourier transformation and non-linear analysis via Poincare plots. The root mean square of successive differences (RMSSD), percent of R-R intervals that differ by more than 50 ms (pRR50), and the standard deviation of R-R intervals (SDRR) were calculated to assess HRV in the time-domain. Fast Fourier transformation was used to assess HRV in the frequency domain (low-frequency (LF) and high-frequency (HF) power). Non-linear HRV analysis was performed using Poincare plots to calculate standard deviation 1 (SD1) and 2 (SD2). We used one-tailed unpaired t-tests to determine if the main outcome variables were different between groups. Values are reported as mean ± SD. RESULTS RRI was lower in THC (THC: 917±109 vs. CON: 1018±141 ms; P=0.06). RMSSD was lower in THC (THC: 57±21 vs. CON: 74±24 ms; P=0.07). There was no difference between groups for pRR50 (THC: 36±20 vs. CON: 47±21 %; P=0.12) or SDRR (THC: 68±16 vs. CON: 78±24 ms; P=0.15). LF power was not different between groups (THC: 1105±850 vs. CON: 1321±852 ms2; P=0.31), but HF power was lower in THC (THC: 1353±925 vs. CON: 2256±1195 ms2; P=0.05). SD1 was lower in THC (THC: 40±15 vs. CON: 53±17 ms; P=0.07). SD2 was not different between groups (THC: 86±19 vs. CON: 96±33 ms; P=0.23). CONCLUSION These preliminary data indicate that college-aged adults who regularly use cannabis have lower heart rate variability than their non-cannabis user counterparts. Based on linear and non-linear analyses, it appears that cannabis users have altered cardiac vagal activity at rest. However, more research is warranted to determine if cannabis use alters autonomic function and reactivity.

Linear and Nonlinear Heart Rate Variability Analysis in Gonadal Dysgenesis (Swyer Syndrome): A Case Report

Swyer syndrome is one of the disorders of sexual differentiation. Previous studies have demonstrated increased sympathetic activity with heart rate variability (HRV) analysis with decreasing estradiol levels. One patient presented a pure 46, XY gonadal dysgenesis with female phenotype. Cardiac autonomic modulation was assessed through HRV analysis while at rest. This research analyzed linear and nonlinear indexes. HRV analysis showed reduced parasympathetic and global modulation with an apparent increase in sympathetic tone and a loss of HR fractal dynamics [...]

On the Variability of Heart Rate Variability-Evidence from Prospective Study of Healthy Young College Students.

Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers.

Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolbox. The heartbeats in ECG were detected using the Pan–Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.