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 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: 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 Noninvasive method for electrocardiogram recording in conscious rats: feasibility for heart rate variability analysis

2010 ◽  
Vol 82 (2) ◽  
pp. 431-437 ◽  
Author(s):  
Pedro P. Pereira-Junior ◽  
Moacir Marocolo ◽  
Fabricio P. Rodrigues ◽  
Emiliano Medei ◽  
José H.M. Nascimento
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Conscious State ◽  
Heart Rate Variability Analysis ◽  
Platinum Electrodes ◽  
Noninvasive Method ◽  
Conscious Rats ◽  
Custom Made ◽  
Surface Ecg ◽  
Hrv Analysis

Heart rate variability (HRV) analysis consists in a well-established tool for the assessment of cardiac autonomic control, both in humans and in animal models. Conventional methods for HRV analysis in rats rely on conscious state electrocardiogram (ECG) recording based on prior invasive surgical procedures for electrodes/transmitters implants. The aim of the present study was to test a noninvasive and inexpensive method for ECG recording in conscious rats, assessing its feasibility for HRV analysis. A custom-made elastic cotton jacket was developed to fit the rat's mean thoracic circumference, with two pieces of platinum electrodes attached on its inner surface, allowing ECG to be recorded noninvasively in conscious, restrained rats (n=6). Time- and frequency-domain HRV analyses were conducted, under basal and autonomic blockade conditions. High-quality ECG signals were obtained, being feasible for HRV analysis. As expected, mean RR interval was significantly decreased in the presence of atropine (p <0.05) and increased in the presence of propranolol (p<0.001). Also, reinforcing the reliability of the method, low- and high-frequency HRV spectral powers were significantly decreased in the presence of propranolol (p <0.05) and atropine (p< 0.001), respectively. In summary, the present work describes a novel, inexpensive and noninvasive method for surface ECG recording in conscious rats.

scholarly journals Autonomic Cardiac Activity in Patients with Smoking and Alcohol Addiction by Heart Rate Variability Analysis

2016 ◽  
Vol 39 (6) ◽  
pp. 147 ◽  
Author(s):  
Ramazan Yuksel ◽  
Rabia Nazik Yuksel ◽  
Tijen Sengezer ◽  
Senol Dane
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiac Rhythm ◽  
Cardiac Activity ◽  
Alcohol Addiction ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Dsm Iv ◽  
Hrv Analysis ◽  
Almost All

Purpose: Smoking and alcohol addictions are common and worldwide. In the present study, we aimed to investigate the effects of these addictions on cardiac rhythm using heart rate variability (HRV) analysis. Methods: Addicts (n=42 men: 22 cigarette; 20 cigarette and alcohol) and age-matched controls (n=34 men) were included in the study. All patients fulfill the criteria for dependence according to DSM-IV-TR. Electrocardiography (ECG) recordings were obtained for a total of 30 minutes. Fagerstrom Nicotine Addiction Test (FNAT) and CAGE questionnaire (Cut down, Annoy, Guilt, Eye opener) was applied to all patients. Results: Almost all HRV parameters were significantly decreased in cigarette and cigarette and alcohol addicts compared with controls (p

scholarly journals Validity of the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition and Fitness Level Groups

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 902
Author(s):  
Adrián Hernández-Vicente ◽  
David Hernando ◽  
Jorge Marín-Puyalto ◽  
Germán Vicente-Rodríguez ◽  
Nuria Garatachea ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Body Composition ◽  
Excellent Agreement ◽  
Exercise Intensity ◽  
Heart Rate Variability Analysis ◽  
Fat Percentage ◽  
Fitness Level ◽  
Hrv Analysis ◽  
Frequency Power

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried out to evaluate how these phenotypic characteristics influenced HR and HRV measurements. For this purpose, sixty-seven volunteers performed a test consisting of the following consecutive segments: sitting rest, three submaximal exercise intensities in cycle-ergometer and sitting recovery. The agreement between HRV indices derived from Polar H7 and a simultaneous electrocardiogram (ECG) was assessed using concordance correlation coefficient (CCC). The percentage of subjects not reaching excellent agreement (CCC > 0.90) was higher for high-frequency power (PHF) than for low-frequency power (PLF) of HRV and increased with exercise intensity. A cluster of unfit and not young volunteers with high trunk fat percentage showed the highest error in HRV indices. This study indicates that Polar H7 and ECG were interchangeable at rest. During exercise, HR and PLF showed excellent agreement between devices. However, during the highest exercise intensity, CCC for PHF was lower than 0.90 in as many as 60% of the volunteers. During recovery, HR but not HRV measurements were accurate. As a conclusion, phenotypic differences between subjects can represent one of the causes for disagreement between HR sensors and ECG devices, which should be considered specifically when using Polar H7 and, generally, in the validation of any HR sensor for HRV analysis.

scholarly journals Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3870 ◽  
Author(s):  
Keisuke Kamata ◽  
Koichi Kinoshita ◽  
Manabu Kano
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Least Squares ◽  
Partial Least Squares ◽  
Health Monitoring ◽  
Normal Sinus Rhythm ◽  
Local Model ◽  
Heart Rate Variability Analysis ◽  
Modeling Framework ◽  
Hrv Analysis

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

scholarly journals My Therapist is a Student? The Impact of Therapist Experience and Client Severity on Cognitive Behavioural Therapy Outcomes for People with Anxiety Disorders

2015 ◽  
Vol 44 (2) ◽  
pp. 193-202 ◽  
Author(s):  
Liam Mason ◽  
Nick Grey ◽  
David Veale
Keyword(s):  
Anxiety Disorders ◽  
Behavioural Therapy ◽  
Outcome Data ◽  
Symptom Improvement ◽  
Therapist Experience ◽  
Clinically Significant Change ◽  
Trainee Therapists ◽  
Significant Change ◽  
Clinically Significant ◽  
The Impact

Background: Allocation of trainee therapist cases is often performed based on intuition and clinical circumstances, with lack of empirical evidence on the role of severity of presenting problem. This has the potential to be anxiety-provoking for supervisors, trainees and service users themselves. Aims: To determine how therapist experience interacts with symptom severity in predicting client outcomes. Method: An intention-to-treat analysis of annual outcome data for primary and secondary care clients seen by a specialist anxiety disorders service. 196 clients were stratified into mild, moderate and baseline severe symptoms of anxiety (GAD-7) and depression (PHQ-9). We measured percentage change on these measures, as well as number of sessions and therapy dropout. We also examined rates of reliable and clinically significant change on disorder-specific measures. We hypothesized that qualified therapists would achieve better outcomes than trainees, particularly for severe presentations. Results: Overall, outcomes were comparable between trainee and qualified therapists on all measures, and trainees additionally utilized fewer therapy sessions. There was however an interaction between anxiety severity (GAD-7) and therapist group, such that severely anxious clients achieved greater symptom improvement with qualified as compared to trainee therapists. Further, for trainee but not qualified therapists, baseline anxiety was negatively associated with rate of reliable and clinically significant change on disorder-specific measures. Conclusions: These findings indicate generally favourable outcomes for trainee therapists delivering manualized treatments for anxiety disorders. They additionally suggest that trainee therapists may benefit from additional support when working with clients that present with severe anxiety.

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