Heart Rate Complexity Research Articles

Changes in Maternal Heart Rate Variability and Photoplethysmography Morphology after Corticosteroid Administration: A Prospective, Observational Study.

Background: Owing to the association between dysfunctional maternal autonomic regulation and pregnancy complications, assessing non-invasive features reflecting autonomic activity-e.g., heart rate variability (HRV) and the morphology of the photoplethysmography (PPG) pulse wave-may aid in tracking maternal health. However, women with early pregnancy complications typically receive medication, such as corticosteroids, and the effect of corticosteroids on maternal HRV and PPG pulse wave morphology is not well-researched. Methods: We performed a prospective, observational study assessing the effect of betamethasone (a commonly used corticosteroid) on non-invasively assessed features of autonomic regulation. Sixty-one women with an indication for betamethasone were enrolled and wore a wrist-worn PPG device for at least four days, from which five-minute measurements were selected for analysis. A baseline measurement was selected either before betamethasone administration or sufficiently thereafter (i.e., three days after the last injection). Furthermore, measurements were selected 24, 48, and 72 h after betamethasone administration. HRV features in the time domain and frequency domain and describing heart rate (HR) complexity were calculated, along with PPG morphology features. These features were compared between the different days. Results: Maternal HR was significantly higher and HRV features linked to parasympathetic activity were significantly lower 24 h after betamethasone administration. Features linked to sympathetic activity remained stable. Furthermore, based on the PPG morphology features, betamethasone appears to have a vasoconstrictive effect. Conclusions: Our results suggest that administering betamethasone affects maternal autonomic regulation and cardiovasculature. Researchers assessing maternal HRV in complicated pregnancies should schedule measurements before or sufficiently after corticosteroid administration.

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

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

The effects of mechanical ventilation on heart rate variability and complexity in mice

In a variety of diseases, altered respiratory modulation is often an early sign of autonomic dysfunction. Therefore, understanding and evaluating the effects of artificial ventilation on the autonomic nervous system is vital. The effects of artificial ventilation on autonomic balance have been assessed by heart rate variability using frequency domain and non-linear analysis including fractal complexity and entropy analysis in anesthetized mice. BALB/c mice (n=48) were divided into two groups: Spontaneous breathing and artificial ventilation. The electrocardiograms were recorded. Four different analyses were used: i. frequency domain analysis, ii. Poincaré plots, iii. DFA and iv. Entropy analysis. An unpaired t-test was used for statistical analysis. In a ventilated group, VLF and LF parameters were not changed, whereas the HF parameter was decreased compared to spontaneous breathing mice. DFAα1 was significantly increased due to artificial ventilation but DFAα2 was unchanged. SD2/SD1 ratio was increased, however, SD1 and SD2 were not significantly affected. Also, ApEn and SampEn remained unchanged. HF parameter, DFAα1, and SD2/SD1 were affected by artificial ventilation. Decreased HF and increased DFAα1, further support the notion that HRV is dominated by respiratory sinus arrhythmia at high frequencies, this may be due to decreased vagal tone caused by artificial ventilation.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

BackgroundIn intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2. MethodsWe conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm’s performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability. Measurements and main resultsThe study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2. ConclusionsThe AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

Inter-day reliability of heart rate complexity and variability metrics in healthy highly active younger and older adults

PurposeTo investigate the inter-day reliability of time-domain, frequency-domain, and nonlinear HRV metrics in healthy highly active younger and older adults. The study also assessed the effect of age on the HRV metrics.MethodsForty-four older adults (34 M, 10F; 59 ± 5 years; V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{{{\ ext{2peak}}}}$$\\end{document} = 40.9 ± 7.6 ml kg−1 min−1) and twenty-two younger adults (16 M, 6F; 22 ± 4 years; V˙O2peak\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{V}{\ ext{O}}_{{{\ ext{2peak}}}}$$\\end{document} = 47.2 ± 12.8 ml kg−1 min−1) attended the laboratory. Visit one assessed aerobic fitness through an exercise test. In visits two and three, participants completed a 30-min supine RR interval measurement to derive the HRV metrics.ResultsThe younger group (YG) and older group (OG) demonstrated poor to good day-to-day relative and absolute reliability for all HRV metrics (OG, ICCs = 0.33 to 0.69 and between day CVs = 3.8 to 29.2%; YG, ICCs = 0.37 to 0.93 and between day CVs = 3.5 to 36.5%). There was a significant reduction in ApEn (P < 0.001), SampEn (P = 0.031), RMSSD (P < 0.001), SDNN (P < 0.001), LF power (P < 0.001) and HF power (P < 0.001), HRV metrics with ageing. There was no significant effect of age the complexity metrics DFA α1 (P = 0.107), α2 (P = 0.147) and CI-8 (P = 0.493).ConclusionHRV metrics are reproducible between days in both healthy highly active younger and older adults. There is a decline in linear and nonlinear HRV metrics with age, albeit there being no age-related change in the nonlinear metrics, DFA α1, α2 and CI-8.

Comparison of Heart Autonomic Control between Hemodynamically Stable and Unstable Patients during Hemodialysis Sessions: A Bayesian Approach.

Intradialytic hypotension is a common complication during hemodialysis sessions. The analysis of successive RR interval variability using nonlinear methods represents a promising tool for evaluating the cardiovascular response to acute volemic changes. Thus, the present study aims to compare the variability of successive RR intervals between hemodynamically stable (HS) and unstable (HU) patients during a hemodialysis session, through linear and nonlinear methods. Forty-six chronic kidney disease patients volunteered in this study. Successive RR intervals and blood pressures were recorded throughout the hemodialysis session. Hemodynamic stability was defined based on the delta of systolic blood pressure (higher SBP-lower SBP). The cutoff for hemodynamic stability was defined as 30 mm Hg, and patients were stratified as: HS ([n = 21]: ≤29.9 mm Hg) or HU ([n = 25]: ≥30 mm Hg). Linear methods (low-frequency [LFnu] and high-frequency [HFnu] spectra) and nonlinear methods (multiscale entropy [MSE] for Scales 1-20, and fuzzy entropy) were applied. The area under the MSE curve at Scales 1-5 (MSE1-5), 6-20 (MSE6-20), and 1-20 (MSE1-20) were also used as nonlinear parameters. Frequentist and Bayesian inferences were applied to compare HS and HU patients. The HS patients exhibited a significantly higher LFnu and lower HFnu. For MSE parameters, Scales 3-20 were significantly higher, as well as MSE1-5, MSE6-20, and MSE1-20 in HS, when compared to HU patients (p < 0.05). Regarding Bayesian inference, the spectral parameters demonstrated an anecdotal (65.9%) posterior probability favoring the alternative hypothesis, while MSE exhibited moderate to very strong probability (79.4 to 96.3%) at Scales 3-20, and MSE1-5, MSE6-20, and MSE1-20. HS patients exhibited a higher heart-rate complexity than HU patients. In addition, the MSE demonstrated a greater potential than spectral methods to differentiate variability patterns in successive RR intervals.

PO-05-133 PROLONGED NEUROMODULATION EFFECTS AFTER PULMONARY VEIN ISOLATION IN PATIENTS WITH PAROXYSMAL ATRIAL FIBRILLATION: INSIGHT FROM THE NONLINEAR HEART RATE DYNAMICS

Heart rate variability (HRV) reveals information undetected by direct electrocardiogram morphology evaluation. Prior research indicated that patients with paroxysmal atrial fibrillation (PAF) had altered a short-term linear HRV after pulmonary vein isolation (PVI). However, the long-term changes in the nonlinear heart rate dynamics before and after PVI remain unclear. The objective was to investigate the long-term nonlinear HRV changes before and after PVI in PAF patients. We enrolled 42 drug-refractory symptomatic PAF patients who underwent PVI. Holter monitoring were done less than 1 year before, 1∼3, and 6∼12 months after PVI, respectively. The nonlinear HRV variables, including sample entropy (SampEn), multi-scale entropy (MSE), detrended fluctuation analysis (DFA), and Poincaré Plot analysis, were analyzed. Significant reductions in SD1, SD1/SD2, MSE 1∼5, MSE 6∼20, and SampEn occurred 6∼12 months after when compared to those before PVI, respectively. SD2, MSE slope 5, DFA slope 1 decreased 1∼3 months after PVI when compared to those before PVI, respectively, whereas increased significantly 6∼12 months after PVI when compared to those 1∼3 months after PVI, respectively. DFA slope2 increased significantly after PVI when compared to that before PVI. Detailed parameters are shown in the Table 1. As measured by DFA slope2, fractal correlation properties increased significantly following PVI. Heart rate complexity (MSE 1∼5, MSE 6∼20, and SampEn) and unpredictability of an RR time series (SD1/SD2) decreased significantly after PVI, whereas long-term RR interval (SD2) and short-term complexity (MSE slope 5) decreased in the 1∼3 months after PVI and then increased in 6∼12 months after PVI. These results indicated that the neuromodulation effects from PVI was far more prolonged and extensively than expected.Tabled 1Table 1. Serial changes in the HRV parametersPatients with PAF who underwent PVI (n = 42)< 1 year pre-PVI1∼3 months post-PVI6∼12 months post-PVISD121.16 ± 8.416.31 ± 11.08∗14.19 ± 8.63∗SD2177.58 ± 44.38134.92 ± 37∗146.46 ± 37.52∗+SD1/SD20.12 ± 0.060.12 ± 0.090.1 ± 0.06∗+DFA slope11.11 ± 0.240.8 ± 0.31∗0.94 ± 0.31∗+DFA slope21.07 ± 0.11.16 ± 0.13∗1.15 ± 0.11∗MSE 1∼55.62 ± 1.824.76 ± 1.74∗4.72 ± 1.59∗MSE 6∼2019.81 ± 5.6716.01 ± 5.6∗17.3 ± 4.75∗MSE slope50.06 ± 0.07-0.01 ± 0.09∗0.03 ± 0.07+SampEn1 ± 0.321.01 ± 0.290.9 ± 0.26∗+Data are expressed as (mean ± SD); DFA slope1: Detrended fluctuation analysis slope 1; DFA slope2: Detrended fluctuation analysis slope 2; MSE 1∼5, the summation of entropy values of scales 1 to 5; MSE 6∼20, the summation of entropy values of scales 6 to 20; MSE slope 5, the linear-fitted slope of scales 1 to 5; SD1, Poincaré plot standard deviation perpendicular the line of identity; SD2, Poincaré plot standard deviation along the line of identity; SD1/SD2, SD1-to-SD2 ratio; SampEn, sample entropy; *: p < 0.05 versus < 1 year pre-PVI; +: p < 0.05 versus 1∼3 months post-PVI Open table in a new tab

On the distinct differences in autonomic regulation between pregnant and non-pregnant women - a heart rate variability analysis

Objective. Appropriate adaptation of the maternal autonomic nervous system to progressing gestation is essential to a healthy pregnancy. This is partly evidenced by the association between pregnancy complications and autonomic dysfunction. Therefore, assessing maternal heart rate variability (HRV)—a proxy measure for autonomic activity—may offer insights into maternal health, potentially enabling the early detection of complications. However, identifying abnormal maternal HRV requires a thorough understanding of normal maternal HRV. While HRV in women of childbearing age has been extensively investigated, less is known concerning HRV during pregnancy. Subsequently, we investigate the differences in HRV between healthy pregnant women and their non-pregnant counterparts. Approach. We use a comprehensive suite of HRV features (assessing sympathetic and parasympathetic activity, heart rate (HR) complexity, HR fragmentation, and autonomic responsiveness) to quantify HRV in large groups of healthy pregnant (n = 258) and non-pregnant women (n = 252). We compare the statistical significance and effect size of the potential differences between the groups. Main results. We find significantly increased sympathetic and decreased parasympathetic activity during healthy pregnancy, along with significantly attenuated autonomic responsiveness, which we hypothesize serves as a protective mechanism against sympathetic overactivity. HRV differences between these groups typically had a large effect size (Cohen’s d > 0.8), with the largest effect accompanying the significantly reduced HR complexity and altered sympathovagal balance observed in pregnancy (Cohen’s d > 1.2). Significance. Healthy pregnant women are autonomically distinct from their non-pregnant counterparts. Subsequently, assumptions based on HRV research in non-pregnant women cannot be readily translated to pregnant women.

Heart Rate Variability by Dynamical Patterns in Windows of Holter Electrocardiograms: A Method to Discern Left Ventricular Hypertrophy in Heart Transplant Patients Shortly after the Transplant

Background: The Holter electrocardiogram (ECG) provides a long signal that represents the heart’s responses to both autonomic regulation and various phenomena, including heart tissue remodeling. Loss of information is a common result when using global statistical metrics. Method: Breaking the signal into short data segments (e.g., windows) provides access to transient heart rate characteristics. Symbolization of the ECG by patterns of accelerations and/or decelerations allows using entropic metrics in the assessment of heart rate complexity. Two types of analysis are proposed: (i) visualization of the pattern dynamics of the whole signal, and (ii) scanning the signal for pattern dynamics in a sliding window. The method was applied to a cohort of 42 heart transplant (HTX) recipients divided into the following groups: a left ventricle of normal geometry (NG), concentrically remodeled (CR), hypertrophic remodeled (H), and to the control group (CG) consisting of signals of 41 healthy coevals. The Kruskal–Wallis test was used to assess group differences. Statistical conclusions were verified via bootstrap methods. Results: The visualization of the group pattern dynamics showed severely limited autonomic regulations in HTX patients when compared to CG. The analysis (in segments) prove that the pattern dynamics of the NG group are different from the pattern dynamics observed in the CR and H groups. Conclusion: Dynamic pattern entropy estimators tested in moving windows recognized left ventricular remodeling in stable HTX patients.

Day-night patterns in heart rate variability and complexity: differences with age and cardiopulmonary disease.

Heart rate variability (HRV) measures provide valuable insights into physiology; however, gaps remain in understanding circadian patterns in heart rate dynamics. We aimed to explore day-night differences in heart rate dynamics in patients with chronic cardiopulmonary disease compared with healthy controls. Using 24-hour heart rate data from patients with chronic obstructive pulmonary disease (COPD) and/or heart failure (n = 16) and healthy adult controls (older group: ≥50 years, n = 42; younger group: 20-49 years, n = 136), we compared day-night differences in conventional time and frequency domain HRV indices and a multiscale-entropy-based complexity index (CI1-20) of HRV among the 3 groups. Twenty-four-hour HRV showed significant day-night differences (marked with "△") among younger healthy (mean age: 34.5 years), older healthy (mean age: 61.6 years), and cardiopulmonary patients (mean age: 68.4 years), including change in percentage of adjacent intervals that differ > 50 ms (△pNN50), high frequency (△HF), normalized low frequency (△nLF), ratio (△LF/HF), and △CI1-20. Among these, △LF/HF (2.13 ± 2.35 vs 1.1 ± 2.47 vs -0.35 ± 1.25; P < .001) and △CI1-20 (0.15 ± 0.24 vs 0.02 ± 0.28 vs -0.21 ± 0.27; P < .001) were significant in each pairwise comparison following analysis of variance tests. Average CI1-20 was highest in younger healthy individuals and lowest in cardiopulmonary patients (1.37 ± 0.12 vs 1.01 ± 0.27; P < .001). Younger healthy patients showed a heart rate complexity dipping pattern (night < day), older healthy patients showed nondipping, and cardiopulmonary patients showed reverse dipping (night > day). As measures of 24-hour variability, traditional and complexity-based metrics of HRV exhibit large day-night differences in healthy individuals; these differences are blunted, or even reversed, in individuals with cardiopulmonary pathology. Measures of diurnal dynamics may be useful indices of reduced adaptive capacity in patients with cardiopulmonary conditions. Ma Y, Chang M-C, Litrownik D, Wayne PM, Yeh GY. Day-night patterns in heart rate variability and complexity: differences with age and cardiopulmonary disease. J Clin Sleep Med. 2023;19(5):873-882.

Heart Rate Complexity and Autonomic Modulation Are Associated with Psychological Response Inhibition in Healthy Subjects

the ability to suppress/regulate impulsive reactions has been identified as common factor underlying the performance in all executive function tasks. We analyzed the HRV signals (power of high (HF) and low (LF) frequency, Sample Entropy (SampEn), and Complexity Index (CI)) during the execution of cognitive tests to assess flexibility, inhibition abilities, and rule learning. we enrolled thirty-six healthy subjects, recording five minutes of resting state and two tasks of increasing complexity based on 220 visual stimuli with 12 × 12 cm red and white squares on a black background. at baseline, CI was negatively correlated with age, and LF was negatively correlated with SampEn. In Task 1, the CI and LF/HF were negatively correlated with errors. In Task 2, the reaction time positively correlated with the CI and the LF/HF ratio errors. Using a binary logistic regression model, age, CI, and LF/HF ratio classified performance groups with a sensitivity and specificity of 73 and 71%, respectively. this study performed an important initial exploration in defining the complex relationship between CI, sympathovagal balance, and age in regulating impulsive reactions during cognitive tests. Our approach could be applied in assessing cognitive decline, providing additional information on the brain-heart interaction.

Evaluation of non-linear heart rate variability using multi-scale multi-fractal detrended fluctuation analysis in mice: Roles of the autonomic nervous system and sinoatrial node.

Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo, and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo, the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment.

Chaotic Global Metric Analysis of Heart Rate Variability Following Six Power Spectral Manipulations in Malnourished Children

Background and Aim: The study objective was to assess chaotic global metrics in malnourished children following power spectral manipulations. Methods: We evaluated the complexity of heart rate (HR) variability (HRV) in malnourished subjects via six power spectra (Welch, multi-taper method (MTM), Burg, covariance, Yule-Walker, and periodogram) and then, when adjusted by the MTM parameters, for further refinement. Seventy children were split equally (controls &amp; malnourished) and the HR was monitored for 20 min; 1000 RR-intervals were attained for HRV analysis. Results: The results stipulate that CFP1 (chaotic forward parameter) and CFP3 are the best metrics to distinguish the two groups. The most appropriate power spectra were Welch, MTM, and Yule-Walker. Results indicate that CFP3 calculated using MTM power spectra is the best combination to discriminate between the two groups. Yet, if the RR intervals are set to 400, discrete prolate spheroidal sequences (DPSS) to 3, and Thomson’s nonlinear combination to ‘adaptive’, a greater level of significance can be achieved (Cohen’s ds = -1.57). This significantly outperforms that under default conditions (Glass’s ∆ Delta = -1.06, and Cohen’s ds = -0.95). Conclusion: Malnourished children have a lower response to chaotic global metrics than the control group. CFP3 with the aforementioned settings is the best combination to discriminate between these groups on the basis of RR intervals. It has the greatest significance by Cohen’s ds. Our data suggest impaired autonomic function in malnourished children, which may have consequences for cardiovascular risks.

Nonlinear Heart Rate Dynamics Before and After Paroxysmal Atrial Fibrillation Events.

Heart rate complexity, derived from nonlinear heart rate variability (HRV), has been shown to help predict the outcomes of various diseases. Changes in heart rate complexity before and after paroxysmal atrial fibrillation (PAF) events are unclear. To evaluate changes in heart rate complexity through nonlinear HRV before and after PAF events. We enrolled 65 patients (72 ± 12.34 years old, 31 females) with 99 PAF events who received 24-hour Holter recording, and analyzed nonlinear HRV variables including Poincaré plot analysis, sample entropy (SampEn), and multiscale entropy (MSE). HRV analyses were applied to a 20-minute window before the onset and after the termination of PAF events. HRV parameters were evaluated and compared based on eight different 5-minute time segments, as we divided each 20-minute window into four segments of 5 minutes each. SampEn and MSE1~5 significantly decreased before the onset of PAF events, whereas SampEn, MSE1~5 and MSE6~20 significantly increased after the termination of PAF events. SD1 and SD2, which are nonlinear HRV parameters calculated via Poincaré plot analysis, did not significantly change before the PAF events, however they both decreased significantly after termination. Heart rate complexity significantly decreased before the initiation and increased after the termination of PAF events, which indicates the crucial role of nonlinear heart rate dynamics in the initiation and termination of PAF.

Measuring fractal dynamics of FECG signals to determine the complexity of fetal heart rate

In this research, we study the fetal heart rate from abdominal signals using multi-fractal spectra and fractal analysis. We use the Abdominal and Direct Fetal Electrocardiogram Database contains multichannel fetal electrocardiogram (FECG) recordings obtained from 5 different women in labor, between 38 and 41 weeks of gestation. We apply autocorrelation or power spectral densities (PSD) analysis on these five FECG recordings to estimate the exponent from realizations of these processes and to find out if the signal of interest exhibits a power-law PSD. We perform multi-fractal analysis to discover whether some type of power-law scaling exists for various statistical moments at different scales of these FECG signals. We plot the multi-fractal spectra of this database to compare the width of the scaling exponent for each spectrum. A quantitative analysis commonly known as the Fractal Dimension (FD) using the Higuchi algorithm has been carried out to illustrate the fractal complexity of input signals. Our finding shows that the fractal geometry can be used as a mathematical model and computational framework to further analysis and classification of clinical database. Moreover, it can be considered as a framework to compare the complexity of FECG signals and a useful tool to differentiate between their patterns.

Evolution of Heart Rate Complexity Indices in the Early Detection of Neonatal Sepsis.

Despite advances in prenatal health care, neonatal sepsis remains a major cause of neonatal mortality. Early diagnosis and adequate treatment are essential to reduce morbidity and mortality related to this disease. In this paper, we propose a new method to detect neonatal sepsis based on heart rate (HR) complexity measures (entropy and compression indices) that takes into consideration neonatal gestational age. First, the percentile curves were computed for all the complexity indices using data from 118 control neonates. Eight indices were computed: the sample entropy (SampEn) and three indices to quantify the multiscale entropy (MSE) curve - the sum, the slope, and the product of the previous two - and the compression ratio (CR), using the bzip2 compressor, as well as the same three indices but related to the multiscale compression (MSC) curve. Then, the corresponding percentile was estimated for 23 sepsis neonates. Results show a significant decrease in the entropy indices SampEn and MSEsum and in the MSCslope a day before the detection of sepsis by the clinicians. The indices CR and MSCsum increased before the antibiotic take. These results imply that sepsis causes a random, uncorrelated pattern on the HR signal. Future studies should include a bigger data set to calculate a compound index comprising information of other physiological signals. Clinical Relevance - Prompt and accurate diagnosis of neona-tal sepsis is essential for the successful clinical management of neonates and significantly reduce morbidity and mortality. Complexity measures applied to the HR time series appear to detect sepsis in neonates starting one day before the clinical detection.

Longitudinally Tracking Maternal Autonomic Modulation During Normal Pregnancy With Comprehensive Heart Rate Variability Analyses.

Changes in the maternal autonomic nervous system are essential in facilitating the physiological changes that pregnancy necessitates. Insufficient autonomic adaptation is linked to complications such as hypertensive diseases of pregnancy. Consequently, tracking autonomic modulation during progressing pregnancy could allow for the early detection of emerging deteriorations in maternal health. Autonomic modulation can be longitudinally and unobtrusively monitored by assessing heart rate variability (HRV). Yet, changes in maternal HRV (mHRV) throughout pregnancy remain poorly understood. In previous studies, mHRV is typically assessed only once per trimester with standard HRV features. However, since gestational changes are complex and dynamic, assessing mHRV comprehensively and more frequently may better showcase the changing autonomic modulation over pregnancy. Subsequently, we longitudinally (median sessions = 8) assess mHRV in 29 healthy pregnancies with features that assess sympathetic and parasympathetic activity, as well as heart rate (HR) complexity, HR responsiveness and HR fragmentation. We find that vagal activity, HR complexity, HR responsiveness, and HR fragmentation significantly decrease. Their associated effect sizes are small, suggesting that the increasing demands of advancing gestation are well tolerated. Furthermore, we find a notable change in autonomic activity during the transition from the second to third trimester, highlighting the dynamic nature of changes in pregnancy. Lastly, while we saw the expected rise in mean HR with gestational age, we also observed increased autonomic deceleration activity, seemingly to counter this rising mean HR. These results are an important step towards gaining insights into gestational physiology as well as tracking maternal health via mHRV.

Associations of common genetic risk variants of the muscarinic acetylcholine receptor M2 with cardiac autonomic dysfunction in patients with schizophrenia

Objectives Decreased vagal modulation, which has consistently been observed in schizophrenic patients, might contribute to increased cardiac mortality in schizophrenia. Previously, associations between CHRM2 (Cholinergic Receptor Muscarinic 2) and cardiac autonomic features have been reported. Here, we tested for possible associations between these polymorphisms and heart rate variability in patients with schizophrenia. Methods A total of three single nucleotide polymorphisms (SNPs) in CHRM2 (rs73158705 A>G, rs8191992 T>A and rs2350782 T>C) that achieved significance (p < 5 * 10−8) in genome-wide association studies for cardiac autonomic features were genotyped in 88 drug-naïve patients, 61 patients receiving antipsychotic medication and 144 healthy controls. Genotypes were analysed for associations with parameters of heart rate variability and complexity, in each diagnostic group. Results We observed a significantly altered heart rate variability in unmedicated patients with identified genetic risk status in rs73158705 A>G, rs8191992 T>A and rs2350782 T>C as compared to genotype non-risk status. In patients receiving antipsychotic medication and healthy controls, these associations were not observed. Discussion We report novel candidate genetic associations with cardiac autonomic dysfunction in schizophrenia, but larger cohorts are required for replication.

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue.

Understanding the influence of running-induced acute fatigue on the homeostasis of the body is essential to mitigate the adverse effects and optimize positive adaptations to training. Fatigue is a multifactorial phenomenon, which influences biomechanical, physiological, and psychological facets. This work aimed to assess the evolution of these three facets with acute fatigue during a half-marathon. 13 recreational runners were equipped with one inertial measurement unit (IMU) on each foot, one combined global navigation satellite system-IMU-electrocardiogram sensor on the chest, and an Android smartphone equipped with an audio recording application. Spatio-temporal parameters for the running gait, along with the heart rate, its variability and complexity were computed using validated algorithms. Perceived fatigability was assessed using the rating-of-fatigue (ROF) scale at every 10 min of the race. The data was split into eight equal segments, corresponding to at least one ROF value per segment, and only level running parts were retained for analysis. During the race, contact time, duty factor, and trunk anteroposterior acceleration increased, and the foot strike angle and vertical stiffness decreased significantly. Heart rate showed a progressive increase, while the metrics for heart rate variability and complexity decreased during the race. The biomechanical parameters showed a significant alteration even with a small change in perceived fatigue, whereas the heart rate dynamics altered at higher changes. When divided into two groups, the slower runners presented a higher change in heart rate dynamics throughout the race than the faster runners; they both showed similar trends for the gait parameters. When tested for linear and non-linear correlations, heart rate had the highest association with biomechanical parameters, while the trunk anteroposterior acceleration had the lowest association with heart rate dynamics. These results indicate the ability of faster runners to better judge their physiological limits and hint toward a higher sensitivity of perceived fatigue to neuromuscular changes in the running gait. This study highlights measurable influences of acute fatigue, which can be studied only through concurrent measurement of biomechanical, physiological, and psychological facets of running in real-world conditions.

Heart rate complexity: An early prognostic marker of patient outcome after cardiac arrest

ObjectiveEarly prognostication in comatose patients after cardiac arrest (CA) is difficult but essential to inform relatives and optimize treatment. Here we investigate the predictive value of heart-rate variability captured by multiscale entropy (MSE) for long-term outcomes in comatose patients during the first 24 hours after CA. MethodsIn this retrospective analysis of prospective multi-centric cohort, we analyzed MSE of the heart rate in 79 comatose patients after CA while undergoing targeted temperature management and sedation during the first day of coma. From the MSE, two complexity indices were derived by summing values over short and long time scales (CIs and CIl). We splitted the data in training and test datasets for analysing the predictive value for patient outcomes (defined as best cerebral performance category within 3 months) of CIs and CIl. ResultsAcross the whole dataset, CIl provided the best sensitivity, specificity, and accuracy (88%, 75%, and 82%, respectively). Positive and negative predictive power were 81% and 84%. ConclusionsCharacterizing the complexity of the ECG in patients after CA provides an accurate prediction of both favorable and unfavorable outcomes. SignificanceThe analysis of heartrate variability by means of MSE provides accurate outcome prediction on the first day of coma.