Time-domain Analysis Of Heart Rate Variability Research Articles

Essential Fatty Acid Associated with Heart Rate Variability in Highly Trained Male Cross-Country Skiers: A Pilot Study

Polyunsaturated fatty acid (PUFA) metabolites play important roles in the modulation of vascular tone, heart rate variability (HRV), and cardiovascular diseases. This study was undertaken to examine the relationship between HRV and the plasma levels of essential acids. Methods: Highly trained cross-country skiers participated in the study (n = 19). Time-domain and frequency-domain HRV analyses were performed. The plasma levels of fatty acids were determined using gas–liquid chromatography. Results: Plasma eicosapentaenoic acid and docosahexaenoic acid were found to be negatively correlated with resting heart rate (HR) (p = 0.026). The plasma levels of alpha-linolenic acid (ALA) were positively associated with the relative value of high-frequency power (rs = 0.465, p = 0.045) and negatively correlated with the sympathovagal balance ratio (rs = −0.493, p = 0.032) and the absolute and relative values of low-frequency power (rs = −0.490, p = 0.028). The plasma levels of arachidonic acid (ARA) were positively associated with the relative value of high-frequency power (rs = 0.59, p = 0.006) and negatively correlated with the sympathovagal balance ratio (rs = −0.54, p = 0.017) and the relative values of low-frequency power (rs = −0.52, p = 0.022). No correlation was found between n6/n3 and HRV parameters except for HR and pNN50. Conclusions: n-3 PUFAs and ARA play an important role in the autonomic regulation of heart rate in highly trained skiers. Athletes with substantial deficiencies in plasma ALA and excess levels of ARA had increased sympathetic and decreased parasympathetic activity.

Time-domain heart rate variability features for automatic congestive heart failure prediction.

Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non-invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non-invasive biomarker to classify healthy adults and those with heart failure. We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre-processing, we performed time-domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five-fold cross-validation using four machine learning classification algorithms: support vector machine, k-nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92. The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non-invasive HRV, to screen congestive heart failure individuals in primary care.

Response Inhibition Partially Mediates the Relationship Between Emotional States and Creative Divergent Thinking

ABSTRACT Why can some people generate outstanding creative ideas despite receiving frustrating feedback? Although previous studies highlighted the effects of emotional states on creativity, the interactions between specific psychophysiological emotional parameters or affective states and response inhibition (RI) on creativity remain elusive. Therefore, with this study, we aimed to investigate whether RI mediates the effects of emotional states on creative thinking, specifically divergent thinking (DT), while participants receive frustrating or encouraging feedback. We induced positive and negative affect and psychophysiological arousal (PA) by manipulating feedback on performing a go/no-go task (GNGT), one of the standardized tasks for measuring RI. In other words, we provided participants with artificial feedback on GNGT to induce either frustration following (a negative emotional state) or a feeling of success (a positive emotional state). After receiving the manipulated feedback, participants performed the alternative uses task (AUT), a classical test for measuring DT. Subjective affective states were assessed using the Positive and Negative Affect Scale — Expanded Form (PANAS-X). During AUT, PA was measured through skin conductance (SC) and heart rate variability (HRV). Our data revealed that RI mediates the effect of negative affect and fatigue and enhanced PA (measured through HRV) on DT. Moreover, positive affect and PA (measured through SC) directly enhanced the three indices of DT (fluency, originality, and flexibility). Concerning the measurement of HRV, the application of time-domain HRV analyses was superior to that of frequency-domain HRV analyses. Notably, gender had strong direct and indirect effects on fluency and flexibility but not originality. In conclusion, our results suggest distinct mechanisms for modulatory effects of specific emotional states and associated psychophysiological on divergent creative thinking.

Electrochemical skin conductance and heart rate variability in patients with non-dialysis chronic kidney disease

Chronic kidney disease (CKD) is very common now and associates with high overall and cardiovascular mortality. Numerous studies have reported that Heart rate variability (HRV) could also be used to detect cardiovascular autonomic dysfunction (CAD). We investigated the association of electrochemical skin conductance (ESC) of EZSCAN results with HRV in non-dialysis CKD patients. In a cross-sectional study, we enrolled 248 prevalent non-dialysis CKD patients. Patients underwent a 24-h Holter (CB-2302-A, Bio Instrument, China). A time domain analysis of HRV was performed, and the following parameters were obtained: SDNN, SDANN, rMSSD, pNN50. EZSCAN device (Impeto Medical, Paris, France) measures ESC values of each participants. Mean global skin conductance computed as 0.5 * (reflecting (right + left hand)/2+(right and left foot)/2). Log transforms data into a normal distribution for statistical analysis. There were 142 males and 106 females included in the present study. Patients' age was 56.6±17.08years. Logarithm(Log) (global ESC) was independently predicted by age (P<0.01), hypertension history, estimated Glomerular filtration rate (eGFR) and log SDNN (P<0.05). While log SDANN, rMSSD and pNN50 were not independent predictors for log (global ESC). Increased global ESC significantly associated with elevated HRV, specifically SDNN in non-dialysis CKD patients. This suggested that global ESC may appear to be an important predictor of CAD, and even could be used as a cardiovascular risk factor in non-dialysis CKD patients.

Persistent Increase of Sympathetic Activity in Post-Acute COVID-19 of Paucisymptomatic Healthcare Workers.

Healthcare workers (HCWs) represent a population with a significant burden of paucisymptomatic COVID-19, as the general population. We evaluated autonomic nervous system activity by means of heart rate variability (HRV) in HCWs during health surveillance visits. Short-term electrocardiogram (ECG) recordings were obtained 30 days (IQR 5.25-55.75) after a negative naso-pharyngeal swab for SARS-CoV-2 in 44 cases and compared with ECGs of 44 controls with similar age and sex distribution. Time and frequency domain HRV were evaluated. HCWs who used drugs, had comorbidities that affected HRV, or were hospitalized with severe COVID-19 were excluded. Frequency domain HRV analysis showed a significantly higher low/high-frequency power ratio (LF/HF) in the case study compared with controls (t = 2.84, p = 0.006). In time domain HRV analysis, mean standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive RR interval differences (RMSSD) were significantly lower for cases compared with controls (t = -2.64, p = 0.01 and t = -3.27, p = 0.002, respectively). In the post-acute phase of infection, SARS-CoV-2 produces an autonomic imbalance mirrored by a reduction in HRV. These results are consistent with epidemiological data that suggest a higher risk of acute cardiovascular complications in the first 30 days after COVID-19 infection.

The Importance of Time-Domain HRV Analysis in Cardiac Health Prediction

Heart rate variability (HRV) is defined as the momentary variation in the end heart rate (EHR) estimated at various intervals (time domains), such as from 2 min (ultra-short HRV) to 24 h (long HRV) intervals. The R peak interval (RRI) between two consecutive beats called momentary heart rate (MHR) provides insight into the impending cardiovascular risk and not the EHR. The autonomic nervous system (ANS) is in charge of maintaining physiological homeostasis by keeping the MHR and in turn the EHR within the normal range of 60–100 bpm. ANS has two components – the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). The former increases HR (reduces RRI) while the latter reduces it. Therefore, the RR time-domain-HRV-data (THD) provides better insight into overall health than the EHR. Six types of THDs, e.g., mean-HR, mean-RR, SDNN, SDHR, RMSSD, and pNN50 are discussed in this article.

Influence of Geopathic Stress Zone on Heart Rate variability Parameters

Background and Aim:&nbsp;Geopathic stress (GS)is the effect of detrimental earth radiation and electro-magnetic radiation on the health of human body.&nbsp;The aim of this study is to evaluate the effect of the geopathic stress on heart rate variability(HRV) parameters in healthy human subjects.&nbsp;Methods:&nbsp;The time and frequency &nbsp;domain HRV analyses were performed to assess the changes in sympathovagal &nbsp;balance in group of 60 healthy volunteers with normal electrocardiogram(ECG).Sixty volunteers we-re selected for the study with &nbsp;no history of illness(40 healthy men volunteers, 20 healthy women vo-lunteers). By using the apparatus Rayocomp PS-10(German, Rayonex), Geopathic stress zone were measured. The time and frequency domain HRV analyses were performed befor and after 60 minutes stay of volunteers in Geopathic stress zone. &nbsp;Results:&nbsp;During 60 minutes stay on the GS zone, the time domain parameters such as standard &nbsp;deviation(SD, ms) of all normal sinus RR intervals were significantly decreased. Then frequency domain parameters such as very low frequency (VLF, ms2), low frequency (LF, ms), and the LF/HF ratio were significantly increased. Conclusion:&nbsp;The study indicated&nbsp;&nbsp;that the stay of healthy&nbsp;human&nbsp;in the GS zone may influence heart rate variability and change the sympathovagal&nbsp;balance.

Effect of Stress on Heart Rate Variability in Medical Students: A Cross-sectional Study

Introduction: Medical education can impose significant psychological stress on undergraduate students. A considerable degree of psychological morbidity has been reported among medical students ranging from stress, interpersonal problems, and suicidal ideation to psychiatric disorders and they tend to have greater psychological distress than the general population. Stress is one of the factors known to cause variation in heart rate variability. Heart Rate Variability (HRV) is a proven reliable non invasive marker of cardiovascular health and has been used in cardiovascular risk stratification. Aim: To study the effect of stress on heart rate variability in medical students. Materials and Methods: The cross-sectional study was conducted in the Department of Physiology at JSS Medical College, Mysuru, Karnataka, India, from December 2017 to February 2018. Total 58 healthy first-year MBBS medical students aged between 18-25 years with normal Body Mass Index (BMI) were included in the study. The Medical Student Stressor Questionnaire (MSSQ), a validated instrument to identify sources of stress in medical students was used to assess the level of stress. AD Instruments PowerLab (Data Acquisition System) was used to record the frequency and time domain analysis of HRV from the limb leads. Kruskal-Wallis test was applied to assess the association between stress domains and HRV domains, using Statistical Package for Social Sciences (SPSS) version 23.0. Results: Most of the medical students had mild to moderate degrees of stress in all the domains of the MSSQ. There was no statistically significant (p-value&gt;0.05) association found between domains of stress with the time and frequency domains of the HRV. Conclusion: Though it was found that medical students were exposed to significant stressors during their medical training, the stress was of mild to a moderate degree which had no significant effect on HRV.

Аssessment of vegetative regulation of the heart rate in patients with pulmonary TB

SummaryAim. To evaluate features of vegetative regulation of the heart rate in patients with pulmonary TB. Methods. The authors analyzed examination results of 109 patients with different forms of pulmonary TB (the main group). The comparison group consisted of 63 individuals with-out TB or heart disease. To identify features of vegetative regulation of the heart rate, all patients underwent 3-lead 24-hour ECG monitoring and assessment of heart rate variability (HRV) indices using spectral and time-domain analyses. The results were processed using MS Excel 2016 for Windows and Statistica 10 software programmes; nonparametric methods were used. Results. According to our research data, pulmonary TB patients had signifi-cantly lower values of the time-domain analysis of HRV: SDNN in 28% of cases (р&lt;0.05); RMSSD in 22% of cases (р&lt;0.05). The spectral analysis data showed HF in 91% of cases (р&lt;0.05), LF in 61% of cases (р&lt;0.05), a rigid circa-dian rhythm of the heart rate in 55% of cases (p&lt;0.05), and the increased index of vagosympathetic balance in 93% of cases (р&lt;0.05). The sympathicotonic type of vegetative regulation was detected in 93% of cases, the balanced initial vegetative type of HRV — in 5% of cases, and the parasympathicotonic type — in 2% of cases. The authors established a direct strong correlation between indices of RMSSD and HF, r=0.81 (p&lt;0.05). The changes in HRV parameters were not linked with clinical forms of TB (р&gt;0.05). Conclusion. Initial sympathicotonia prevails in patients with pulmonary TB, which indicates intensive work of the cardiovascular system, which in its turn leads to the increased risk of adverse cardiac events. Patients with pulmonary TB should undergo dynamic 24-hour Holter monitoring for timely correction of vegetative re-gulation of the heart rate.

Association between Levels of Urine Di-(2-ethylhexyl)phthalate Metabolites and Heart Rate Variability in Young Adults.

Phthalate exposure is associated with cardiovascular risk. Among the various phthalates, di-(2-ethylhexyl) phthalate (DEHP) is a deleterious plasticizer in our daily lives. This study investigated the association between DEHP exposure and the alteration of heart rate variability (HRV). During 2017–2019, we recruited 974 young adults to investigate the effects of living environments and dietary habits on cardiometabolic disorders in Taiwan. We quantitatively analyzed urinary metabolites of DHEP. A continuous electrocardiogram was recorded to obtain a 5-min ECG. Time-domain and frequency-domain HRV analyses were performed. Multiple linear regression showed that urinary oxidized DEHP metabolites MEHHP and MEOHP were associated with decreased HRV after controlling for associated cardiovascular risk factors. A higher MEHHP level was associated with a lower triangular interpolation of NN interval histogram (TINN), very low frequency (VLF), and low frequency/high frequency (LF/HF) ratio. A higher MEOHP level was associated with a decreased LF/HF ratio. In addition, trend analysis showed that higher MEHHP and MEOHP quantiles were significantly associated with a decreased LF/HF ratio. DEHP is a potentially harmful and invisible chemical. The urinary DEHP metabolites MEHHP and MEOHP are associated with decreased HRV, indicating an adverse effect on autonomic balance in young adults in Taiwan.

Heart Rate Variability Analyses in Parkinson's Disease: A Systematic Review and Meta-Analysis.

Recent evidence suggests that the vagus nerve and autonomic dysfunction play an important role in the pathogenesis of Parkinson’s disease. Using heart rate variability analysis, the autonomic modulation of cardiac activity can be investigated. This meta-analysis aims to assess if analysis of heart rate variability may indicate decreased parasympathetic tone in patients with Parkinson’s disease. The MEDLINE, EMBASE and Cochrane Central databases were searched on 31 December 2020. Studies were included if they: (1) were published in English, (2) analyzed idiopathic Parkinson’s disease and healthy adult controls, and (3) reported at least one frequency- or time-domain heart rate variability analysis parameter, which represents parasympathetic regulation. We included 47 studies with 2772 subjects. Random-effects meta-analyses revealed significantly decreased effect sizes in Parkinson patients for the high-frequency spectral component (HFms2) and the short-term measurement of the root mean square of successive normal-to-normal interval differences (RMSSD). However, heterogeneity was high, and there was evidence for publication bias regarding HFms2. There is some evidence that a more advanced disease leads to an impaired parasympathetic regulation. In conclusion, short-term measurement of RMSSD is a reliable parameter to assess parasympathetically impaired cardiac modulation in Parkinson patients. The measurement should be performed with a predefined respiratory rate.

Heart Rate Variability in Young Adults with Persisting Post‐Concussion Symptoms

Objective Recovery following a concussion generally occurs within 7 to 10 days. However, between 10 to 50% of adults experience persistent post-concussion symptoms (PPCS). Alterations in cardiac autonomic function, which may lead to heart rate (HR) dysregulation, could explain some of those persisting symptoms. Our aim was to examine short-term resting heart rate variability (HRV), commonly used as an indicator of cardiac autonomic balance, in a group of subjects with PPCS. Hypothesis HRV parameters associated with cardiac vagal tone will be lower, while parameters associated with cardiac sympathetic activity (SYMP) will be higher in subjects with PPCS when compared to controls. Methods HRV was quantified in individuals experiencing PPCS for more than 4 wks [n=9 (5M/4F); 25 ± 6 y; body mass index (BMI): 25 ± 4 kg/m2] compared to matched healthy controls [n=18 (10M/8F); 27 ± 5 y; BMI: 23 ± 3 kg/m2]. Electrical activity of the heart was recorded continuously for 5 min in resting supine position (3-lead ECG). Signals were reviewed for artifacts and processed using Ensemble-R software (Elucimed, Wellington, NZ). Time-domain analysis of HRV provided the square root of the mean squared differences of successive NN intervals (RMSSD), and the number of interval differences, between successive intervals, greater than 50ms (pNN50). Using spectral power analysis, relative low frequency (LF) power, relative high frequency (HF) power and LF/HF ratio were obtained. RMSSD, pNN50 and relative HF power are generally associated with cardiac vagal tone, whilst relative LF power and LF/HF ratio reflect a mix of cardiac SYMP and vagal activity. Results Mean LF/HF ratio was elevated in PPCS compared to controls (2.4 ± 1.9 vs. 0.9 ± 0.5; p=0.020), owing mainly to a lower relative HF power (28.1 ± 15.8 % vs. 48.5 ± 15.8 %; p=0.004), whilst relative LF power did not differ between groups (p=0.053). There were no significant differences in RMSSD and PNN50 between groups. Conclusion As hypothesized, LF/HF ratio was increased. This shift in cardiac autonomic balance appears to result from a reduction in cardiac vagal tone in young adults with PPCS when assessed in the frequency domain.

Remote monitoring can predict lethal arrhythmic events through time-domain analysis of heart rate variability in patients with implantable cardioverter defibrillator

Abstract Background We prospectively collected device and heart rate data, i.e. heart rate variability (HRV), through remote monitoring (RM) of patients with implantable cardioverter defibrillator (ICD). Several studies have demonstrated usefulness of RM in implanted device patients, however, reports concerning the predictors of lethal ventricular arrhythmias are limited. Purpose The objective was to identify the predictors of lethal arrhythmic events (VT/VF). Methods Thirty-three patients (mean age: 50 years) with ICDs [with functionality of HRV analysis] were divided into 2 groups [VT/VF(+), VT/VF(−)]. Clinical, device (ventricular lead impedance; amplitude of ventricular electrogram), and HRV data were compared between the 2 groups. As the index of time-domain HRV analysis, NN intervals-index (SDNNi) was calculated for every 5 minutes, and the mean, maximum, and minimum SDNNi during the 24-hour period were used. Results During the observation period (median 12 months), 10 patients experienced VT/VF events. In HRV data, the mean, max, and min SDNNi were higher in VT/VF(+) than VT/VF(−) group (132.9±9.3 v.s. 93.5±6.1, p=0.0013; 214.6±10.6 v.s. 167.0±7.0, p=0.0007; 71.2±7.5 v.s. 43.9±4.9, p=0.0047). The other parameters did not exhibit significant difference. On logistic regression analysis, the mean SDNNi of 100.1, max SDNNi of 185.0 and min SDNNi of 52.0 as cut-off values for prediction of VT/VF event demonstrated significant receiver operating characteristics (ROC) curves (AUC=0.86, p=0.0007; AUC=0.84, p=0.0005; AUC=0.78, p=0.0030). Furthermore, in cases of VT/VF(+) group, the max ΔSDNNi, i.e., difference from baseline SDNNi, and min ΔSDNNi in 7 and 28 days preceding VT/VF events exhibited time course changes in comparison with baseline values. They were significant predictors of VT/VF events (max ΔSDNNi cut-off: 46.8, AUC=0.91, p=0.0002; min ΔSDNNi cut-off: −42.4, AUC=0.88, p=0.0014). Conclusion Time-domain analysis of HRV through RM may help identify patients at high risk of lethal arrhythmic events, and predict occurrence of such arrhythmic events. Funding Acknowledgement Type of funding source: None

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.

Heart rate variability and recovery following maximal exercise in endurance athletes and physically active individuals.

The purpose of this study was to determine potential adverse cardiac effects of chronic endurance training by comparing sympathovagal modulation via heart rate variability (HRV) and heart rate recovery (HRR) in middle-aged endurance athletes (EA) and physically active individuals (PA) following maximal exercise. Thirty-six (age, 53 ± 5 years) EA and 19 (age, 56 ± 5 years) PA were recruited to complete a 2-week exercise diary and graded exercise to exhaustion. Time domain and power spectral HRV analyses were completed on recorded R-R intervals. EA had a greater HRR slope following exercise (95% confidence interval, 0.0134-0.0138 vs. 0.0101-0.0104 beats/s; p < 0.001). While EA had greater HRR at 1-5 min after exercise (all p < 0.01), PA and EA did not differ when expressed as a percentage of baseline heart rate (130 ± 19 vs. 139 ± 19; p = 0.2). Root mean square of successive differences in R-R intervals (rest and immediately after exercise) were elevated in EA (p < 0.05). Low-frequency (LF) and high-frequency (HF) spectral components were nonsignificantly elevated after exercise (p = 0.045-0.147) in EA while LF/HF was not different (p = 0.529-0.986). This data suggests greater HRR in EA may arise in part due to a lower resting HR. While nonsignificant elevations in HF and LF in EA produces a LF/HF similar to PA, absolute spectral component modulation differed. These observations require further exploration. Novelty Acute effects of exercise on HRV in EA compared with a relevant control group, PA, are unknown. EA had greater HRR and nonsignificant elevations in LF and HF compared with PA, yet LF/HF was not different. Future work should explore the implications of this observation.

Effectiveness of sleep surgery versus a mandibular advancement device for obstructive sleep apnea in terms of nocturnal cardiac autonomic activity

Sleep surgery and mandibular advancement devices (MAD) are treatments for obstructive sleep apnea (OSA), but their comparative efficacy remains unclear. We compared their efficacy using various parameters. Subjects treated for OSA with sleep surgery or MAD (n= 30/group)-matched for sex, body mass index (BMI), and baseline apnea-hypopnea index (AHI)-were enrolled. The efficacy of these treatments according to polysomnographic parameters, sleep quality questionnaires, and heart rate variability (HRV) time- and frequency-domain parameters were compared between pre-treatment and 3-month post-treatment. Polysomnographic and sleep quality questionnaire parameters improved significantly in both groups. In time-domain HRV analysis, average normal-to-normal intervals increased significantly in the surgery (942.2±140.8 to 994.6±143.1, P= 0.008) and MAD (901.1±131.7 to 953.7±123.1, P= 0.002) groups. Low frequency (LF) decreased significantly in the surgery group (P= 0.012); high frequency (HF) remained unchanged in both groups. The LF/HF ratio decreased in both groups (2.9± 1.8 to 2.3 ± 1.7, P= 0.017, vs. 3.0± 1.8 to 2.4 ± 1.4, P= 0.025). Normalized high frequency increased significantly in both groups (31.0 ± 13.2 to 36.8 ± 13.7, P= 0.009, vs. 29.1 ± 10.7 to 33.7 ± 12.5, P= 0.024), in contrast to normalized low frequency. However, no HRV parameter changes differed significantly between the groups after adjusting for age, BMI, and AHI. Sleep surgery and MAD are equally effective treatments for OSA according to cardiac autonomic activity.

Early identification of cardiac autonomic neuropathy using complexity analysis in children with type 1 diabetes.

In children and adolescents, there are significant limitations to detecting cardiac autonomic neuropathy (CAN), an important contributor to morbidity and mortality in adults with type 1 diabetes (T1D). The analysis of heart rate variability (HRV) is one method available to detect CAN. Some evidence shows traditional linear HRV measures detect abnormalities in youth with T1D. In this study, we aimed to assess whether non-linear complexity analysis of HRV would assist identification of CAN in youth with T1D and to assess contributory factors. We studied 19 youth with T1D and 17 healthy controls. Each had an electrocardiogram recorded continuously for 10 min, at a sampling frequency of 1000 Hz. Using Labview software and an algorithm for complexity analysis, along with standard time-domain and spectral analysis, recordings of the electrocardiogram were analysed to detect differences in HRV between groups. Youth with T1D had significantly higher sample entropy than controls (P = 0.015) suggesting increased complexity in HRV, but similar detrended fluctuation analysis (P = 0.68). Youth with T1D also had increased % high frequency power (P = 0.017) and reduced mid-frequency power (P = 0.019) on spectral analysis. There were no differences in heart rate or blood pressure responses to standing, or time-domain analysis of HRV. Within the T1D group, sample entropy correlated strongly with triglycerides (r = 0.76, P = 0.001) and detrended fluctuation analysis correlated strongly with serum potassium (r = -0.86, P < 0.001). Complexity analysis of HRV, particularly using sample entropy, may aid detection of CAN in youth with T1D.

Heart rate and heart rate variability comparison between postural orthostatic tachycardia syndrome versus healthy participants; a systematic review and meta-analysis

BackgroundA number of published literature has reported that, physiologically, heart rate variability (HRV) in patients with postural orthostatic tachycardia syndrome (POTS) to be greatly confounded by age, sex, race, physical fitness, and circadian rhythm. The purpose of this study was to compare between POTS patients versus healthy participants, in terms of heart rate (HR) and HRV after Head-Up tilt test (HUTT), by systematic review and meta-analysis of available published literature.MethodsMEDLINE (using PubMed interphase), EMBASE and SCOPUS were systematically searched for observational studies comparing POTS patients versus healthy patients, in terms of HR and HRV. HRV was grouped into Time and frequency domain outcome measurements. The time domain was measured as mean RR- interval and mean the square root of the mean of squares of successive R-R waves (rMSSD) in milliseconds. The frequency domain was measured as mean values of Low frequency power (LF), High frequency power (HF), LF/HF-ratio, LF-normalized units (LF(n.u)) and HF-normalized units (HF(n.u)). Demographic data, comorbidities, and mean values of HR, RR- interval, rMSSD, LF, HF, LF/HF-ratio, LF-(n.u) and H.F-n.u were extracted from each group and compared, by their mean differences as an overall outcome measure. Computer software, RevMan 5.3 was utilized, at a 95% significance level.ResultsTwenty (20) eligible studies were found to report 717 POTS and 641 healthy participants. POTS group had a higher mean HR (p < 0.05), lower mean RR-Interval (p < 0.05), lower rMSSD (p < 0.05) than healthy participants. Furthermore, POTS group had lower mean HF(p > 0.05), lower mean LF(p > 0.05), and lower mean HF(n.u) (p > 0.05), higher LF/HF-Ratio (p > 0.05) and higher LF(n.u) (p > 0.05) as compared to healthy participants.ConclusionPOTS patients have a higher HR than healthy patients after HUTT and lower HRV in terms of time domain measure but not in terms of frequency domain measure. HR and time domain analyses of HRV are more reliable than frequency domain analysis in differentiating POTS patients from the healthy participants. We call upon sensitivity and specificity studies.

A Novel Algorithm for HRV Estimation from Short-Term Acoustic Recordings at Neck.

Heart rate variability (HRV) is an important noninvasive parameter to monitor the activity of the autonomic nervous system. This paper proposes an algorithm to analyze HRV by processing the acoustic data, recorded by placing a small, wearable sensor on the suprasternal notch (at neck) of an adult subject, primarily intended to record breathing sounds. The method used an empirical data analysis approach of the Hilbert-Huang transform (HHT) to construct an instantaneous energy envelope and segment the cardiac cycle by detecting S1 and S2 sounds using the K-means algorithm. The time-domain HRV analysis for the short-term recordings of 10 subjects demonstrated a close agreement with the reference ECG signal. The instantaneous heart rate (IHR) comparisons yielded an accuracy of 95.78% and 92.35% for S1 and S2 sounds respectively. The experimental results showed that the proposed algorithm can provide an accurate HRV analysis for the cardiac signals recorded at the neck.

The Use of Percent Change in RR Interval for Data Exclusion in Analyzing 24-h Time Domain Heart Rate Variability in Rodents.

While epidemiological data support the link between reduced heart rate variability (HRV) and a multitude of pathologies, the mechanisms underlying changes in HRV and disease progression are poorly understood. Even though we have numerous rodent models of disease for mechanistic studies, not being able to reliably measure HRV in conscious, freely moving rodents has hindered our ability to extrapolate the role of HRV in the progression from normal physiology to pathology. The sheer number of heart beats per day (>800,000 in mice) makes data exclusion both time consuming and daunting. We sought to evaluate an RR interval exclusion method based on percent (%) change of adjacent RR intervals. Two approaches were evaluated: % change from “either” and “both” adjacent RR intervals. The data exclusion method based on standard deviation (SD) was also evaluated for comparison. Receiver operating characteristic (ROC) curves were generated to determine the performance of each method. Results showed that exclusion based on % change from “either” adjacent RR intervals was the most accurate method in identifying normal and abnormal RR intervals, with an overall accuracy of 0.92–0.99. As the exclusion value increased (% change or SD), the sensitivity (correctly including normal RR intervals) increased exponentially while the specificity (correctly rejecting abnormal RR intervals) decreased linearly. Compared to the SD method, the “either” approach had a steeper rise in sensitivity and a more gradual decrease in specificity. The intersection of sensitivity and specificity where the exclusion criterion had the same accuracy in identifying normal and abnormal RR intervals was 10–20% change for the “either” approach and ∼ 1 SD for the SD-based exclusion method. Graphically (tachogram and Lorenz plot), 20% change from either adjacent RR interval resembled the data after manual exclusion. Finally, overall (SDNN) and short-term (rMSSD) indices of HRV generated using 20% change from “either” adjacent RR intervals as the exclusion criterion were closer to the manual exclusion method with lower subject-to-subject variability than those generated using the 2 SD exclusion criterion. Thus, 20% change from “either” adjacent RR intervals is a good criterion for data exclusion for reliable 24-h time domain HRV analysis in rodents.