Standard Deviation Of Heart Rate Research Articles

SPHYNCS: Feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over one year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a compliance metric to evaluate the usability of the wearable device, we found an overall compliance rate of 80% over one year. We calculated daily physical activity, heart rate, and sleep parameters from two weeks of greatest compliance to compare NT1 (n=20) and HC (n=9) subjects. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p=0.007) and awakening index (p=0.025), as well as standard deviation of time in bed (p=0.044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p=0.019), and sleep latency (p=0.001), as well as a lower peak heart rate (p=0.008), heart rate standard deviation (p=0.039) and high-intensity activity (p=0.009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Application of remote monitoring of biosignals and geolocation with a Wearable for patients with sequelae of the coronavirus

<span lang="EN-US">Several patients who have overcome coronavirus disease (COVID-19) have been left with cardiovascular and pulmonary sequelae and most medical centers lack a remote monitoring system for each patient that notifies them of any complications during rehabilitation. The objective of this research was to implement a Wearable that monitors the patient's health and alerts in case of detecting any anomaly. For this reason, a Wearable was developed that displays the patient's heart rate, oxygen saturation level and body temperature on the Light Emitting Diode (LCD) and the application mobile, sending an alert and geolocation message if anomalies are detected in vital signs. The standard deviation of heart rate, temperature and oxygen saturation was obtained, which was 1.4930, 0.1558 and 0.4364 in the rest stage, respectively, and 6.3442, 0.2365 and 0.9186 in the physical activity stage respectively with a maximum duration of 42 hours and 52 minutes of battery, managing to send alert messages and store the information in the cloud, which allows to conclude that the Wearable can facilitate the management of the database and the location of the patient, that the measurement error increases with physical activity, and that battery life varies with the number of biosignal readings per hour.</span>

The impact of visit-to-visit heart rate variability on all-cause mortality in atrial fibrillation.

We aimed to investigate the association between visit-to-visit heart rate variability (VVHRV) and all-cause mortality in patients diagnosed with atrial fibrillation (AF). Previous studies have shown a positive correlation between VVHRV and several adverse outcomes. However, the relationship between VVHRV and the prognosis of AF remains uncertain. In our study, we aimed to examine the relationship between VVHRV and mortality rates among 3983 participants with AF, who were part of the AFFIRM study (Atrial Fibrillation Follow-Up Investigation of Rhythm Management). We used the standard deviation of heart rate (HRSD) to measure VVHRV and divided the patients into four groups based on quartiles of HRSD (1st, <5.69; 2nd, 5.69-8.00; 3rd, 8.01-11.01; and 4th, ≥11.02). Our primary endpoint was all-cause death, and we estimated the hazard ratios for mortality using the Cox proportional hazard regressions. Our analysis included 3983 participants from the AFFIRM study and followed for an average of 3.5 years. During this period, 621 participants died from all causes. In multiple-adjustment models, we found that the lowest and highest quartiles of HRSD independently predicted an increased risk of all-cause mortality compared to the other two quartiles, presenting a U-shaped relationship (1st vs 2nd, hazard ratio = 2.28, 95% CI = 1.63-3.20, p < .01; 1st vs. 3rd, hazard ratio = 2.23, 95% CI = 1.60-3.11, p < .01; 4th vs. 2nd, hazard ratio = 1.82, 95% CI = 1.26-2.61, p < .01; and 4th vs. 3rd, hazard ratio = 1.78, 95% CI = 1.25-2.52, p < .01). In patients with AF, we found that both lower VVHRV and higher VVHRV increased the risk of all-cause mortality, indicating a U-shaped curve relationship.

Changes in autonomic function and cerebral and somatic oxygenation with arterial flow pulsatility for children requiring veno-arterial extracorporeal membrane oxygenation

BackgroundVeno-arterial extracorporeal membrane oxygenation (VA-ECMO) carries variability in arterial flow pulsatility (AFP). Research questionWhat changes in cerebral and somatic oxygenation, hemodynamics, and autonomic function are associated with AFP during VA-ECMO? MethodsThis is a prospective study of children on VA-ECMO undergoing neuromonitoring. AFP was quantified by arterial blood pressure pulse amplitude and subcategorized: no pulsatility (<1 mmHg), minimal pulsatility (1 to <5 mmHg), moderate pulsatility (5 to <15 mmHg) and high pulsatility (≥15 mmHg). CVPR was assessed using the cerebral oximetry index (COx). Cerebral and somatic oxygenation was assessed using cerebral regional oximetry (rSO2) or peripheral oxygen saturation (SpO2). Autonomic function was assessed using baroreflex sensitivity (BRs), low-frequency high-frequency (LF/HF) ratio and standard deviation of heart rate R–R intervals (HRsd). Differences were assessed across AFP categories using linear mixed effects models with Tukey pairwise comparisons. Univariate logistic regression was used to explore risk of AFP with brain injuries. ResultsAmong fifty-three children, comparisons of moderate to high pulsatility were associated with reductions in rSO2 (p < 0.001), SpO 2 (p = 0.005), LF/HF ratio (p = 0.028) and an increase in HRsd (p < 0.001). Reductions in BRs were observed across comparisons of none to minimal (P < 0.001) and minimal to moderate pulsatility (p = 0.004). Comparisons of no to low pulsatility were associated with reductions in BRs (p < 0.001) and ABP (p < 0.001) with increases in SpO2 (p < 0.001) and HR (p < 0.001). Arterial ischemic stroke was associated with higher pulsatility (p = 0.0384). ConclusionDuring VA-ECMO support, changes toward high AFP are associated with autonomic dysregulation and compromised cerebral and somatic tissue oxygenation.

Statistical heart rate variability analysis for healthy person: Influence of gender and body posture.

Short-term ECG-derived heart rate variability can assess autonomic function non-invasively. The purpose of this study is to investigate the influence of body posture and gender on parasympathetic-sympathetic balance by utilising electrocardiogram (ECG). A total of sixty participants including thirty males (95% CI: 23.34-26.32years old) and thirty females (95% CI: 23.33-26.07years old) voluntarily executed three sets of 5-min ECG recordings in supine, sitting and standing posture. A nonparametric Friedman test followed by Bonferroni post-hoc test was carried out to find the statistical differences between the group. A significant difference was observed for RR mean, low frequency (LF), high frequency (HF), ratio LF/HF and the ratio long term variability to short term variability (SD2/SD1) for p<0.01 while respiration rate (Resp Rate), standard deviation of heart rate (STD_HR), long term variability (SD2), approximate entropy (ApEn), correlation dimension (CD) are non-significant (p>0.01) for supine, sitting and standing. HRV indices such as standard deviation of NN (SDNN), HRV triangular index (HRVi), and triangular interpolation of NN interval (TINN) are statistically not significant for males but there are significant differences for females at a significance level 1%. Relative reliability and relatedness were evaluated through the interclass coefficient (ICC) and spearman correlation coefficient. The experimental results advocate that there is a posture-specific difference in HRV indices while the correlational studies suggest no such significant differences.

Heart Rate Variability in Normotensive and Hypertensive Adults After a Year of Receiving Oxford/AstraZeneca COVID-19 Vaccine: A Cross-Sectional Observational Study.

Background and aim Heart rate variability (HRV) helps in assessing the autonomic nervous system's function, which has been implicated in cardiovascular disease risk. HRV has been found to be deranged in hypertension. In addition, studies have shown that COVID-19 infection and vaccination can affect HRV. However, the long-term effect of HRV on hypertension has not been explored after COVID-19 vaccination. The objective of this study was to observe the HRV in hypertensive adults after one year of receiving the Oxford/AstraZeneca COVID-19 vaccine and to compare it with normotensive adults. Methods The study included 105 normotensives (blood pressure below 120/80 mmHg) and 75 hypertensive participants who had received the Oxford/AstraZeneca COVID-19 vaccine one year prior. HRV was measured using the PowerLab system (ADInstruments) with the participants in a sitting posture. The HRV parameters assessed included the time domain, frequency domain, and nonlinear measures. Data were presented in descriptive and inferential statistical terms, and the parameters of two groups of individuals were compared by either an unpaired t-test or the Mann-Whitney U test. Results A total of 105 normotensive participants with a mean age of 42.51 ± 9.28 years and 75 hypertensive participants with a mean age of 44.24 ± 10.19 years comprised the sample (p=0.24). Normotensive individuals had a higher standard deviation of RR intervals, a higher coefficient of variation of RR intervals, a higher standard deviation of heart rate, and a higher percentage of successive differences in RR intervals in the time domain. They also had higher values of very low-frequency power, low-frequency (LF) power, and high-frequency (HF) power in the frequency domain. The LF/HF ratio was not significantly different between the two groups. In nonlinear analysis, SD2, a measure of long-term heart rate variability, was higher in normotensive individuals. Conclusion The Oxford/AstraZeneca COVID-19 vaccine did not have a significant effect on HRV parameters in normotensive and hypertensive adults one year after vaccination. However, changes in HRV parameters were observed between supine and standing positions, suggesting the importance of postural changes in HRV assessment.

Visit-to-Visit Heart Rate Variability in the Prediction of Clinical Outcomes of Patients with Atrial Fibrillation.

Visit-to-visit heart rate variability (VVV-HR) has been associated with adverse cardiovascular outcomes. We aimed to determine the predictive value of VVV-HR for adverse clinical outcomes in patients with nonvalvular atrial fibrillation (AF). We used data from a prospective multicenter AF registry of 27 hospitals in Thailand during 2014 to 2017. After the baseline visit, patients were followed up every 6 months until 3 years. VVV-HR was calculated from the standard deviation of heart rate data from baseline visit and every follow-up visit. VVV-HR was categorized into four groups according to the quartiles. Clinical outcomes were all-cause death, ischemic stroke/systemic embolism (SE), and heart failure. Cox proportional hazard models were used for multivariable analysis. There were a total of 3,174 patients (mean age: 67.7 years; 41.8% female). The incidence rates of all-cause death, ischemic stroke/SE, and heart failure were 3.10 (2.74-3.49), 1.42 (1.18-1.69), and 2.09 (1.80-2.42) per 100 person-years respectively. The average heart rate was 77.8 ± 11.0 bpm and the average of standard deviation of heart rate was 11.0 ± 5.9 bpm. VVV-HR Q4 was an independent predictor of all-cause death, ischemic stroke/SE, and heart failure with adjusted hazard ratios of 1.45 (95% confidence interval: 1.07-1.98), 2.02 (1.24-3.29), and 2.63 (1.75-3.96), respectively. VVV-HR still remained a significant predictor of clinical outcomes when analyzed based on coefficient of variation and variability independent of mean. VVV-HR is an independent predictor for adverse clinical outcomes in patients with AF. A J-curve appearance was demonstrated for the effect of VVV-HR on all-cause death.

Heart Rate and Pulse Oximetry Dynamics in the First Week after Birth in Neonatal Intensive Care Unit Patients and the Risk of Cerebral Palsy.

Infants in the neonatal intensive care unit (NICU) are at high risk of adverse neuromotor outcomes. Atypical patterns of heart rate (HR) and pulse oximetry (SpO2) may serve as biomarkers for risk assessment for cerebral palsy (CP). The purpose of this study was to determine whether atypical HR and SpO2 patterns in NICU patients add to clinical variables predicting later diagnosis of CP. This was a retrospective study including patients admitted to a level IV NICU from 2009 to 2017 with archived cardiorespiratory data in the first 7 days from birth to follow-up at >2 years of age. The mean, standard deviation (SD), skewness, kurtosis and cross-correlation of HR and SpO2 were calculated. Three predictive models were developed using least absolute shrinkage and selection operator regression (clinical, cardiorespiratory and combined model), and their performance for predicting CP was evaluated. Seventy infants with CP and 1,733 controls met inclusion criteria for a 3.8% population prevalence. Area under the receiver operating characteristic curve for CP prediction was 0.7524 for the clinical model, 0.7419 for the vital sign model, and 0.7725 for the combined model. Variables included in the combined model were lower maternal age, outborn delivery, lower 5-minute Apgar's score, lower SD of HR, and more negative skewness of HR. In this study including NICU patients of all gestational ages, HR but not SpO2 patterns added to clinical variables to predict the eventual diagnosis of CP. Identification of risk of CP within the first few days of life could result in improved therapy resource allocation and risk stratification in clinical trials of new therapeutics. · SD and skewness of HR have some added predictive value of later diagnosis of CP.. · SpO2 measures do not add to CP prediction.. · Combining clinical variables with early HR measures may improve the prediction of later CP..

Development and Validation of a Novel Score for Predicting Paroxysmal Atrial Fibrillation in Acute Ischemic Stroke.

Atrial fibrillation (AF)—whether paroxysmal or sustained—increases the risk of stroke. We developed and validated a risk score for identifying patients at risk of paroxysmal atrial fibrillation (pAF) after acute ischemic stroke (AIS). A total of 6033 patients with AIS who received 24 h Holter monitoring were identified in the Chang Gung Research Database. Among the identified patients, 5290 with pAF and without AF were included in the multivariable logistic regression analysis to develop the pAF prediction model. The ABCD-SD score (Age, Systolic Blood pressure, Coronary artery disease, Dyslipidemia, and Standard Deviation of heart rate) comprises age (+2 points for every 10 years), systolic blood pressure (−1 point for every 20 mmHg), coronary artery disease (+2 points), dyslipidemia (−2 points), and standard deviation of heart rate (+2 points for every 3 beats per minute). Overall, 5.2% (274/5290) of patients had pAF. The pAF risk ranged from 0.8% (ABCD-SD score ≤ 7) to 18.3% (ABCD-SD score ≥ 15). The model achieved an area under the receiver operating characteristic curve (AUROCC) of 0.767 in the model development group. The ABCD-SD score could aid clinicians in identifying patients with AIS at risk of pAF for advanced cardiac monitoring.

Statistical heart rate variability analysis under rest and post-exercise

Heart rate variability (HRV) analysis indicates the physical occurrence of variation of inter-beat interval and can be employed for regular monitoring of the health of the sportsperson. For the present work, 62 datasets (two from each participant) have been incorporated who were actively engaged in some sort of morning exercise or games to investigate about chronic fatigue and underperformance due to overtraining. Data were acquired by using BIOPAC MP45 and pre-processed signals have been applied for R peak detection using maximum overlap discrete wavelet transform (MODWT). Analysis of variance (ANOVA) and Wilcoxon signed-rank test have been evaluated to differentiate HRV parameters in both resting and post-exercise conditions. The p-value based on ANOVA for each HRV indices suggests that there is no statistically significant difference between both sets of data and it accedes to the null hypothesis but significant differences have been attained for the standard deviation of heart rate and approximate entropy in the case of the Wilcoxon signed-rank test. The statistically significant difference in resting and post-exercise data may be due to overtraining involved during exercise which is a very common issue for athletes and sportspersons. Overtraining can be monitored with the help of biosignals non-invasively.

Statistical heart rate variability analysis under rest and post-exercise

Heart rate variability (HRV) analysis indicates the physical occurrence of variation of inter-beat interval and can be employed for regular monitoring of the health of the sportsperson. For the present work, 62 datasets (two from each participant) have been incorporated who were actively engaged in some sort of morning exercise or games to investigate about chronic fatigue and underperformance due to overtraining. Data were acquired by using BIOPAC MP45 and pre-processed signals have been applied for R peak detection using maximum overlap discrete wavelet transform (MODWT). Analysis of variance (ANOVA) and Wilcoxon signed-rank test have been evaluated to differentiate HRV parameters in both resting and post-exercise conditions. The p-value based on ANOVA for each HRV indices suggests that there is no statistically significant difference between both sets of data and it accedes to the null hypothesis but significant differences have been attained for the standard deviation of heart rate and approximate entropy in the case of the Wilcoxon signed-rank test. The statistically significant difference in resting and post-exercise data may be due to overtraining involved during exercise which is a very common issue for athletes and sportspersons. Overtraining can be monitored with the help of biosignals non-invasively.

Physiologic Characteristics of Hyperosmolar Therapy After Pediatric Traumatic Brain Injury.

All work was performed at the Barrow Neurological Institute at Phoenix Children's Hospital.Objective: Investigate injury severity, neuroimaging, physiology, and outcomes with bolus hyperosmolar therapy (HT) of 3% hypertonic saline or mannitol.Methods: Retrospective cohort analysis was performed. Physiologic variables included intracranial pressure (ICP), arterial blood pressure (ABP), and heart rate (HR). Volume-pressure compensation (PVC) indices included ICP pulse amplitude (AMP) and correlation of AMP and ICP (RAP). Cerebrovascular pressure reactivity (CVPR) indices included pressure reactivity index (PRx), pulse amplitude index (PAx), wavelet PRx (wPRx), and correlation of AMP and cerebral perfusion pressure (RAC). Heart rate variability (HRV) indices included heart rate standard deviation (HRsd), heart rate root mean square of successive differences (HRrmssd) and low-high frequency ratio (LHF). Outcome was assessed using Glasgow Outcomes Scale Extended Pediatrics, 12-months post-injury. Generalized estimating equations was applied to investigate associations of physiologic changes and pre-treatment indices with HT efficacy. Repeated measures analysis of variance was applied to investigate changes after HT without intracranial hypertension (ICH). Wilcoxon rank-sum was applied to investigate HT responsiveness with age, injury severity, neuroimaging, and outcomes.Results: Thirty children received bolus HT. ICH reduction after HT was associated with reduced ICP (p = 0.0064), ABP (p = 0.0126), PRx (p = 0.0063), increased HRsd (p = 0.0408), and decreased pretreatment RAC (p = 0.0115) and wPRx (p = 0.0072). HT-responsive patients were older and had improved outcomes (p = 0.0394). HT without ICH was associated with increased ICP (P < 0.0001) and ABP (P < 0.0001), increases in all HRV indices and decreases in all PVC indices.Conclusion: After pediatric TBI, efficacious HT is associated with decreased ICP and ABP, pre-treatment indices suggesting efficient CVPR, and potentially improved outcomes.

Cerebral cortical autonomic connectivity in low-risk term newborns.

The mature central autonomic network includes connectivity between autonomic nervous system brainstem centers and the cerebral cortex. The study objective was to evaluate the regional connectivity between the cerebral cortex and brainstem autonomic centers in term newborns by measuring coherence between high-density electroencephalography and heart rate variability as measured by electrocardiography. Low-risk term newborns with birth gestational age of 39-40weeks were prospectively enrolled and studied using time-synced electroencephalography and electrocardiography for up to 60min before discharge from the birth hospital. The ccortical autonomicc nervous system association was analyzed using coherence between electroencephalography-delta power and heart rate variability. Heart rate variability measured the parasympathetic tone (root mean square of successive differences of heart rate) and sympathetic tone (standard deviation of heart rate). One hundred and twenty-nine low-risk term infants were included. High coherence delta-root mean square of successive differences was found in central, bitemporal, and occipital brain regions, with less robust coherence delta-standard deviation in the central region and bitemporal areas. Our findings describe a topography of ccortical autonomicc connectivity present at term in low-risk newborns, which was more robust to parasympathetic than sympathetic brainstem centers and was independent of newborn state.

Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study.

BackgroundFrailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population.ObjectiveThe objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible.MethodsFrom June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty.ResultsA total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88).ConclusionsWe proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services.

A Review of the Methods for Sudden Cardiac Death Detection: A Guide for Emergency Physicians

&lt;p class="Abstract"&gt;Sudden cardiac death (SCD) is an unexpected death of a person with or without knowing cardiac causes are often occurring in less than an hour after the incidence of symptoms. In the case of physicians' knowledge of this incident, they can make appropriate decisions for the patients at-risk and reduce the number of such deaths significantly. The purpose of this paper is to examine different methods for predicting sudden cardiac death using electrocardiogram (ECG) signal from 1998 to recent years that can contribute to researchers to become familiar with the wide range of research conducted in this field.&lt;/p&gt;&lt;p class="Abstract"&gt;In this paper, studies using various methods to predict sudden cardiac death that has applied the data from the Physionet and MIT-BIH databases with a sampling frequency of 256 samples per second are reviewed. Both types of data have normal and abnormal sampling labels and the data recording time varies from a few seconds to minutes. In the field of SCD prediction, various studies have addressed the processing of the electrocardiogram (ECG) signal as well as the heart rate variability (HRV) signal in different domains, including time, time-frequency, and nonlinear domain. In time-domain processing the statistical characteristics of time signal such as the mean and standard deviation of heart rate, the mean and standard deviation of RR intervals, and Root Mean Square of the Successive Differences (RSSD) are used. Also, in the frequency domain, the power spectral density (PSD) of the signal energy is used in a very-low-frequency band, low-frequency band and, high-frequency band. Similarly, in the nonlinear domain, features such as Poincare plot, detrended fluctuation analysis (DFA), common entropy, wavelet transform coefficients (WTC), and features of the recursive graph including Lmax, Lmean, correlation dimension (CD), etc. are used. In all of the proposed algorithms so far, researchers have been trying to inform the sudden death alarm in a larger interval than the time of death by separating the signals into different time periods and extracting various features.&lt;/p&gt;&lt;p class="Abstract"&gt;To evaluate the results of the proposed methods, each of the researchers analyzed the a-few-minute intervals before the SCD. Different classification methods are available to identify the efficiency of the proposed algorithm, such as support vector machine (SVM), multilayer perceptron neural network (MLP), radial base function neural network (RBF), k-nearest neighbor (KNN) and mixture expert (ME). The use of features introduced in different domains and different classifiers has led to the observation of different horizons of prediction in various studies. The results of these predictions are often free from the interpretations of clinical symptoms, and their maximum presented time with acceptable validity eventually reaches 4 minutes before the event, which is not an acceptable time for people who have attacked outside the hospital. Accordingly, the most prominent of these evaluations is the mixture expert methodology in which the best feature extraction methods are used in a new method for selecting the optimal feature space locally. This method makes it possible to select different features every minute before the event by choosing the optimal features for each one-minute interval of the signal as an episode which increases the prediction time from 4 minutes before the death to 12 minutes and allows the interpretation of clinical symptoms in terms of multiplication of the presence of the features per minute.&lt;/p&gt;&lt;p class="Abstract"&gt;Given the non-linear nature of the HRV signal and the similarity of the ECG signal in many time intervals, the use of the HRV signal has become more popular among scholars. The analysis of various studies shows that by approaching the time of death, linear features (time and frequency) can be predictive of death according to the sensible behavior and variations in patients’ signal. Instead of moving away from the death interval, the use of chaotic and non-linear features is more effective. Therefore, a more precise selection of features in this area can be useful for increasing the horizon of prediction of death.&lt;/p&gt;

Multimodal Assessment of Cerebral Autoregulation and Autonomic Function After Pediatric Cerebral Arteriovenous Malformation Rupture.

Management after cerebral arteriovenous malformation (AVM) rupture aims toward preventing hemorrhagic expansion while maintaining cerebral perfusion to avoid secondary injury. We investigated associations of model-based indices of cerebral autoregulation (CA) and autonomic function (AF) with outcomes after pediatric cerebral AVM rupture. Multimodal neurologic monitoring data from the initial 3days after cerebral AVM rupture were retrospectively analyzed in children (< 18years). AF indices included standard deviation of heart rate (HRsd), root-mean-square of successive differences in heart rate (HRrmssd), low-high frequency ratio (LHF), and baroreflex sensitivity (BRS). CA indices include pressure reactivity index (PRx), wavelet pressure reactivity indices (wPRx and wPRx-thr), pulse amplitude index (PAx), and correlation coefficient between intracranial pressure pulse amplitude and cerebral perfusion pressure (RAC). Percent time of cerebral perfusion pressure (CPP) below lower limits of autoregulation (LLA) was also computed for each CA index. Primary outcomes were determined using Pediatric Glasgow Outcome Score Extended-Pediatrics (GOSE-PEDs) at 12months and acquired epilepsy. Association of biomarkers with outcomes was investigated using linear regression, Wilcoxon signed-rank, or Chi-square. Fourteen children were analyzed. Lower AF indices were associated with poor outcomes (BRS [p = 0.04], HRsd [p = 0.04], and HRrmssd [p = 0.00]; and acquired epilepsy (LHF [p = 0.027]). Higher CA indices were associated with poor outcomes (PRx [p = 0.00], wPRx [p = 0.00], and wPRx-thr [p = 0.01]), and acquired epilepsy (PRx [p = 0.02] and wPRx [p = 0.00]). Increased time below LLA was associated with poor outcome (percent time below LLA based on PRx [p = 0.00], PAx [p = 0.04], wPRx-thr [p = 0.03], and RAC [p = 0.01]; and acquired epilepsy (PRx [p = 0.00], PAx [p = 0.00], wPRx-thr [p = 0.03], and RAC [p = 0.01]). After pediatric cerebral AVM rupture, poor outcomes are associated with AF and CA when applying various neurophysiologic model-based indices. Prospective work is needed to assess these indices of CA and AF in clinical decision support.

Contrasting open-loop dynamic characteristics of sympathetic and vagal systems during baroreflex-mediated heart rate control in rats.

Although heart rate (HR) is governed by the sympathetic and parasympathetic nervous systems, a head-to-head comparison of the open-loop dynamic characteristics of the total arc from a baroreceptor pressure input to the HR response has yet to be performed. We estimated the transfer function from carotid sinus pressure input to the HR response (HCSP→HR) before and after bilateral vagotomy (n = 7) as well as before and after the administration of a β-blocker propranolol (n = 8) in anesthetized male Wistar-Kyoto rats. The carotid sinus pressure was perturbed according to a Gaussian white noise signal so that the input power spectra were relatively flat between 0.01 and 1 Hz. The gain plot of HCSP→HR was V-shaped. Vagotomy reduced the dynamic gain at 1 Hz (0.0598 ± 0.0065 to 0.0025 ± 0.0004 beats·min-1·mmHg-1, P < 0.001) but not at 0.01 or 0.1 Hz. β-Blockade reduced the dynamic gain at 0.01 Hz (0.247 ± 0.069 to 0.077 ± 0.017 beats·min-1·mmHg-1, P = 0.020) but not at 0.1 or 1 Hz. We also estimated the efferent limb transfer function from electrical vagal efferent stimulation to the HR response (HVN→HR) under β-blockade conditions. We associated the model parameters of HVN→HR with the mean HR and the standard deviation of HR so that HVN→HR could be estimated based only on the HR data. We finally estimated the neural arc transfer function from a pressure input to efferent vagal nerve activity by dividing HCSP→HR by HVN→HR. The mathematically determined vagal neural arc showed derivative characteristics with its phase near zero radians at the lowest frequency.

Diet-Induced Metabolic Syndrome Reduced Heart Rate Variability and Increased Irregularity and Complexity of Short-Term RR Time Series in Rabbits.

Simple SummaryIn recent years, obesity and metabolic syndrome (MetS) have become more prevalent, owing to increased unhealthy habits and sedentary lifestyles becoming public health problems. Both conditions are linked with a higher prevalence of sudden cardiac death (SCD), but the exact mechanisms are not known. An autonomic nervous system imbalance can produce atrial and ventricular arrhythmias, which cause SCD, and this can be quantified by analyzing heart rate variability (HRV). We investigated HRV using time-domain, frequency-domain and nonlinear analyses during the development of MetS in rabbits and found HRV modifications that could be associated with the higher prevalence of SCD in this pathological condition.Metabolic syndrome (MetS) has been linked to a higher prevalence of sudden cardiac death (SCD), but the mechanisms are not well understood. One possible underlying mechanism may be an abnormal modulation of autonomic activity, which can be quantified by analyzing heart rate variability (HRV). Our aim was to investigate the modifications of short-term HRV in an experimental rabbit model during the time-course of MetS development. NZW rabbits were randomly assigned to a control (n = 10) or a MetS group (n = 13), fed 28 weeks with control or high-fat, high-sucrose diets. After anesthesia, a 15-min ECG recording was acquired before diet administration and at weeks 14 and 28. We analyzed short RR time series using time-domain, frequency-domain and nonlinear analyses. A mixed-model factorial ANOVA was used for statistical analysis. Time-domain analysis showed a 52.4% decrease in the standard deviation of heart rate in animals from the MetS group at week 28, but no changes in the rest of parameters. In the frequency domain, we found a 9.7% decrease in the very low frequency and a 380.0% increase of the low frequency bands in MetS animals at week 28, whereas high frequency remained unchanged. Nonlinear analyses showed increased complexity and irregularity of the RR time series in MetS animals.

Focal epileptic seizures anticipation based on patterns of heart rate variability parameters

Background and ObjectiveHeart rate variability parameters are studied by the research community as potential valuable indices for seizure detection and anticipation. This paper investigates heart activity abnormalities during focal epileptic seizures in childhood. MethodsSeizures affect both the sympathetic and parasympathetic system which is expressed as abnormal patterns of heart rate variability (HRV) parameters. In the present study, a clinical dataset containing 42 focal seizures in long-term electrocardiographic (ECG) recordings from drug-resistant pediatric epileptic patients (with age 8.2 ± 4.3 years) was analyzed. ResultsResults indicate that the time domain HRV parameters (heart rate, SDNN, standard deviation of heart rate, upper envelope) and spectral HRV parameters (LF/HF, normalized HF, normalized LF, total power) are significantly affected during ictal periods. The HRV features were ranked in terms of their relevance and efficacy to discriminate non-ictal/ictal periods and the top-ranked features were selected using the minimum Redundancy Maximum Relevance algorithm for further analysis. Then, a personalized anticipation algorithm based on multiple regression was introduced providing an “epileptic index” of imminent seizures. The performance of the system resulted in anticipation accuracy of 77.1% and an anticipation time of 21.8 s. ConclusionsThe results of this analysis could permit the anticipation of focal seizures only using electrocardiographic signals and the implementation of seizure anticipation strategies for a range of real-life clinical applications.

[PP.15.41] RENAL HEMODYNAMICS AFTER RENAL DENERVATION IN PATIENTS WITH RESISTANT HYPERTENSION AND TYPE 2 DIABETES MELLITUS

Objective: Both the type 2 diabetes mellitus (T2DM) and resistant hypertension (RHTN) are associated with increased of renal sympathetic nerve activity and reduction renal blood flow. The aim of this study was to evaluate effect of renal denervation (RDN) on renal hemodynamics in patients with RHTN and T2DM. Design and method: Forty seven patients with true RHTN and T2DM (mean age 60.0 ± 8.8 years, mean office blood pressure (BP) 169.7 ± 19.1 mmHg, eGRF 74.4 ± 21.7 mL/min/1.73m2 (according to MDRD formula), mean HbA1c 6.7 ± 1.4%, 18 male) were included in single-arm prospective interventional study (detailed protocol was published on ClinicalTrial.gov, number NCT01499810). Office BP measurement, ambulatory 24-hour BP, renal Doppler ultrasound (with measured of renal resistive index (RRI)) and assessment of renal function, HbA1c levels were performed at baseline and follow up. On average, patients were taking 4 (3–5) antihypertensive drugs at optimal dosages, including a diuretic. None of the patients changed the antihypertensive treatments during follow-up. Twelve months follow up was completed by 38 pts. Results: RDN reduced both office BP and 24h-BP at 12 months by 25.4/12.5 mmHg and 12.4/6.9 mmHg, p < 0.01 respectively. The mean values of RRI were no change after RDN. There were increasing RRI in 19% pts, no change in 47% pts and decreasing in 34% pts after 12 months. Decrease in RRI in trunks after 12 months was significantly correlated with decrease both in 24h-diastolic BP (DBP) and night-DBP (r = 0.41, p = 0.03 and r = 0.43, p = 0.03, respectively), as well as with reduction in load of 24h-DBP and night-DBP (r = 0.41, p = 0.04 and r = 0.39, p = 0.04, respectively). Reduction of RRI in segmental arteries was associated with increase in heart rate standard deviation (HRSD) (r = −0.48, p = 0.01 for 24h-HRSD and r = −0.47, h = 0/015 for night-HRSD). Conclusions: Mean values of renal resistive index were not changed within one year after renal denervation in patients with RHTN and T2DM; however, increases in HRSD, reflecting positive effect on the autonomic nervous system, and decreases in diastolic BP were associated with improvement of renal hemodynamics.