scholarly journals Heart rate variability: Measurement and emerging use in critical care medicine

2019 ◽  
Vol 21 (2) ◽  
pp. 148-157 ◽  
Author(s):  
Brian W Johnston ◽  
Richard Barrett-Jolley ◽  
Anton Krige ◽  
Ingeborg D Welters
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
Sinus Node ◽  
Heart Rate Variability Analysis ◽  
Time Interval ◽  
Physiological Basis ◽  
Disease States ◽  
Organ Systems ◽  
Heart Rate Variability Measurement

Variation in the time interval between consecutive R wave peaks of the QRS complex has long been recognised. Measurement of this RR interval is used to derive heart rate variability. Heart rate variability is thought to reflect modulation of automaticity of the sinus node by the sympathetic and parasympathetic components of the autonomic nervous system. The clinical application of heart rate variability in determining prognosis post myocardial infarction and the risk of sudden cardiac death is well recognised. More recently, analysis of heart rate variability has found utility in predicting foetal deterioration, deterioration due to sepsis and impending multiorgan dysfunction syndrome in critically unwell adults. Moreover, reductions in heart rate variability have been associated with increased mortality in patients admitted to the intensive care unit. It is hypothesised that heart rate variability reflects and quantifies the neural regulation of organ systems such as the cardiovascular and respiratory systems. In disease states, it is thought that there is an ‘uncoupling’ of organ systems, leading to alterations in ‘inter-organ communication’ and a clinically detectable reduction in heart rate variability. Despite the increasing evidence of the utility of measuring heart rate variability, there remains debate as to the methodology that best represents clinically relevant outcomes. With continuing advances in technology, our understanding of the physiology responsible for heart rate variability evolves. In this article, we review the current understanding of the physiological basis of heart rate variability and the methods available for its measurement. Finally, we review the emerging use of heart rate variability analysis in intensive care medicine and conditions in which heart rate variability has shown promise as a potential physiomarker of disease.

scholarly journals ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit

2017 ◽  
Vol 32 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Dennis J. Rebergen ◽  
Sunil B. Nagaraj ◽  
Eric S. Rosenthal ◽  
Matt T. Bianchi ◽  
Michel J. A. M. van Putten ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Novel Method

scholarly journals Heart Rate Variability Analysis: How Much Artifact Can We Remove?

2020 ◽  
Vol 17 (9) ◽  
pp. 960-965
Author(s):  
David C. Sheridan ◽  
Ryan Dehart ◽  
Amber Lin ◽  
Michael Sabbaj ◽  
Steven D. Baker
Keyword(s):  
Mental Health ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Heart Rate Variability Analysis ◽  
Time Interval ◽  
Clinically Significant Change ◽  
Hrv Analysis ◽  
Significant Change ◽  
Clinically Significant ◽  
The Impact

Objective Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology.Methods This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered.Results Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed.Conclusion Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.

scholarly journals Heart rate variability and delirium in acute non-cardioembolic stroke: a prospective, cross-sectional, cohort study

2021 ◽  
Author(s):  
Eleonora Rollo ◽  
Jessica Marotta ◽  
Antonio Callea ◽  
Valerio Brunetti ◽  
Catello Vollono ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Cardioembolic Stroke ◽  
Heart Rate Variability Analysis ◽  
Stroke Patients ◽  
Cross Sectional

Abstract Objectives Delirium is an acute fluctuating disorder of attention and awareness. It is associated with autonomic dysfunction and increased mortality. The primary endpoint of our study was to measure autonomic activity in acute stroke patients, by means of heart rate variability analysis, in order to identify autonomic modifications that can predispose to delirium. Methods Patients were consecutively enrolled from the stroke unit. Inclusion criteria were age ≥ 18 years and diagnosis of stroke with onset within the previous 72 h confirmed by neuroimaging. Exclusion criteria were atrial fibrillation, congestive heart failure, and conditions requiring intensive care unit. Patients were evaluated by means of Richmond Agitation Sedation Scale (RASS) and Confusion Assessment Method-Intensive Care Unit (CAM-ICU) at baseline, after 72 h, or when symptoms suggesting delirium occurred. For each patient, ECG was recorded at baseline assessment and HRV analysis was conducted on five consecutive minutes of artifact-free ECG traces. Results Fifty-six ECGs were available for analysis. During the study period, 11 patients developed delirium. Patients with and without delirium did not differ for sex, age, severity of stroke, and comorbidities. The delirium group had greater standard deviation of the heart rate (DLR − :9.16 ± 8.28; DLR + : 14.36 ± 5.55; p = 0.026) and lower power spectral density of the HF component (DLR − : 38.23 ± 19.23 n.u.; DLR + : 25.75 ± 8.77 n.u.; p = 0.031). Conclusions Acute non-cardioembolic stroke patients with increased variability of heart rate and decreased vagal control are at risk for delirium.

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

scholarly journals Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriana Leal ◽  
Mauro F. Pinto ◽  
Fábio Lopes ◽  
Anna M. Bianchi ◽  
Jorge Henriques ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Time ◽  
Epileptic Seizures ◽  
Heart Rate Variability Analysis ◽  
Drug Resistant ◽  
Clustering Methods ◽  
Temporal Lobe Seizures ◽  
New Perspective ◽  
Drug Resistant Epilepsy

AbstractElectrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure’s clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

scholarly journals Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3235
Author(s):  
Koichi Fujiwara ◽  
Shota Miyatani ◽  
Asuka Goda ◽  
Miho Miyajima ◽  
Tetsuo Sasano ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mean Squared Error ◽  
False Positive Rate ◽  
Premature Ventricular Contraction ◽  
Heart Rate Variability Analysis ◽  
Unified Framework ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Positive Rate

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

scholarly journals Heart Rate Variability Analyses in Parkinson’s Disease: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 959
Author(s):  
Konstantin G. Heimrich ◽  
Thomas Lehmann ◽  
Peter Schlattmann ◽  
Tino Prell
Keyword(s):  
Parkinson’S Disease ◽  
Heart Rate ◽  
Parkinson's Disease ◽  
Heart Rate Variability ◽  
Meta Analysis ◽  
Cardiac Activity ◽  
Heart Rate Variability Analysis ◽  
Short Term ◽  
Variability Analysis ◽  
Parasympathetic Regulation

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.

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