Interpretation of Premature Ventricular Abnormality based on HRV Analysis

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.

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HRV analysis: undependability of approximate entropy at locating optimum complexity in malnourished children

Cardiology in the Young ◽

10.1017/s1047951121002316 ◽

2021 ◽

pp. 1-6

Author(s):

David M. Garner ◽

Gláucia S. Barreto ◽

Vitor E. Valenti ◽

Franciele M. Vanderlei ◽

Andrey A. Porto ◽

...

Keyword(s):

Statistical Significance ◽

Accurate Determination ◽

Approximate Entropy ◽

Effect Sizes ◽

Potential Hazard ◽

Autonomic Modulation ◽

Embedding Dimension ◽

Malnourished Children ◽

Hrv Analysis

Abstract Introduction: Approximate Entropy is an extensively enforced metric to evaluate chaotic responses and irregularities of RR intervals sourced from an eletrocardiogram. However, to estimate their responses, it has one major problem – the accurate determination of tolerances and embedding dimensions. So, we aimed to overt this potential hazard by calculating numerous alternatives to detect their optimality in malnourished children. Materials and methods: We evaluated 70 subjects split equally: malnourished children and controls. To estimate autonomic modulation, the heart rate was measured lacking any physical, sensory or pharmacologic stimuli. In the time series attained, Approximate Entropy was computed for tolerance (0.1→0.5 in intervals of 0.1) and embedding dimension (1→5 in intervals of 1) and the statistical significances between the groups by their Cohen’s ds and Hedges’s gs were totalled. Results: The uppermost value of statistical significance accomplished for the effect sizes for any of the combinations was −0.2897 (Cohen’s ds) and −0.2865 (Hedges’s gs). This was achieved with embedding dimension = 5 and tolerance = 0.3. Conclusions: Approximate Entropy was able to identify a reduction in chaotic response via malnourished children. The best values of embedding dimension and tolerance of the Approximate Entropy to identify malnourished children were, respectively, embedding dimension = 5 and embedding tolerance = 0.3. Nevertheless, Approximate Entropy is still an unreliable mathematical marker to regulate this.

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Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology

Sensors ◽

10.3390/s21113746 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3746

Author(s):

Lluis Capdevila ◽

Jesús Castro-Marrero ◽

José Alegre ◽

Juan Ramos-Castro ◽

Rosa M Escorihuela

Keyword(s):

Chronic Fatigue Syndrome ◽

Autonomic Dysfunction ◽

Mobile Health ◽

Health Technology ◽

Chronic Fatigue ◽

Gender Effects ◽

Non Invasive ◽

Fatigue Syndrome ◽

Mhealth Technology ◽

Hrv Analysis

In a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker of ME/CFS using mHealth technology, and evaluates differential gender effects on HRV and ME/CFS core symptoms. In our methodology, participants included 77 ME/CFS patients (32 men and 45 women) and 44 age-matched healthy controls (19 men and 25 women), all self-reporting subjective scores for fatigue, sleep quality, anxiety, and depression, and neurovegetative symptoms of autonomic dysfunction. The inter-beat cardiac intervals are continuously monitored/recorded over three 5-min periods, and HRV is analyzed using a custom-made application (iOS) on a mobile device connected via Bluetooth to a wearable cardiac chest band. Male ME/CFS patients show increased scores compared with control men in all symptoms and scores of fatigue, and autonomic dysfunction, as with women in the first study. No differences in any HRV parameter appear between male ME/CFS patients and controls, in contrast to our findings in women. However, we have found negative correlations of ME/CFS symptomatology with cardiac variability (SDNN, RMSSD, pNN50, LF) in men. We have also found a significant relationship between fatigue symptomatology and HRV parameters in ME/CFS patients, but not in healthy control men. Gender effects appear in HF, LF/HF, and HFnu HRV parameters. A MANOVA analysis shows differential gender effects depending on the experimental condition in autonomic dysfunction symptoms and HF and HFnu HRV parameters. A decreased HRV pattern in ME/CFS women compared to ME/CFS men may reflect a sex-related cardiac autonomic dysfunction in ME/CFS illness that could be used as a predictive marker of disease progression. In conclusion, we show that HRV analysis using mHealth technology is an objective, non-invasive tool that can be useful for clinical prediction of fatigue severity, especially in women with ME/CFS.

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Short-Term HRV Analysis Using Nonparametric Sample Entropy for Obstructive Sleep Apnea

Entropy ◽

10.3390/e23030267 ◽

2021 ◽

Vol 23 (3) ◽

pp. 267

Author(s):

Duan Liang ◽

Shan Wu ◽

Lan Tang ◽

Kaicheng Feng ◽

Guanzheng Liu

Keyword(s):

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Sample Entropy ◽

Biomedical Signal Processing ◽

Apnea Hypopnea Index ◽

Short Term ◽

Autonomic Nervous System Dysfunction ◽

Obstructive Sleep ◽

Hrv Analysis ◽

Frequency Power

Obstructive sleep apnea (OSA) is associated with reduced heart rate variability (HRV) and autonomic nervous system dysfunction. Sample entropy (SampEn) is commonly used for regularity analysis. However, it has limitations in processing short-term segments of HRV signals due to the extreme dependence of its functional parameters. We used the nonparametric sample entropy (NPSampEn) as a novel index for short-term HRV analysis in the case of OSA. The manuscript included 60 6-h electrocardiogram recordings (20 healthy, 14 mild-moderate OSA, and 26 severe OSA) from the PhysioNet database. The NPSampEn value was compared with the SampEn value and frequency domain indices. The empirical results showed that NPSampEn could better differentiate the three groups (p < 0.01) than the ratio of low frequency power to high frequency power (LF/HF) and SampEn. Moreover, NPSampEn (83.3%) approached a higher OSA screening accuracy than the LF/HF (73.3%) and SampEn (68.3%). Compared with SampEn (|r| = 0.602, p < 0.05), NPSampEn (|r| = 0.756, p < 0.05) had a significantly stronger association with the apnea-hypopnea index (AHI). Hence, NPSampEn can fully overcome the influence of individual differences that are prevalent in biomedical signal processing, and might be useful in processing short-term segments of HRV signal.

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HRV analysis in local anesthesia using Continuous Wavelet Transform (CWT)

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2011.6091191 ◽

2011 ◽

Cited By ~ 2

Author(s):

K. Shafqat ◽

S. K. Pal ◽

S. Kumari ◽

P. A. Kyriacou

Keyword(s):

Wavelet Transform ◽

Local Anesthesia ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Hrv Analysis

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Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2016.7591557 ◽

2016 ◽

Cited By ~ 9

Author(s):

R. Castaldo ◽

W. Xu ◽

P. Melillo ◽

L. Pecchia ◽

L. Santamaria ◽

...

Keyword(s):

Mental Stress ◽

Short Term ◽

Hrv Analysis

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Detecting Pedestrian Situation Awareness in Real-Time: Algorithm Development Using Heart Rate Variability and Phone Position

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931218621357 ◽

2018 ◽

Vol 62 (1) ◽

pp. 1579-1583

Author(s):

Yourui Tong ◽

Bochen Jia ◽

Yi Wang ◽

Si Yang

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Real Time ◽

Situation Awareness ◽

Mental State ◽

Time Algorithm ◽

Support Vector ◽

Vehicle Design ◽

Automated Vehicle ◽

Hrv Analysis

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

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