Heart Rate Variability Assessment and Its Application for Autonomic Function Evaluation in Patients with Hyperhidrosis

Background: Pathophysiology mechanism of primary focal hyperhidrosis (PFHH) is controversial. Heart rate variability (HRV) could explain if there is a systemic component present. We aimed to investigate the functions of the autonomic nervous system in patients diagnosed with PFHH compared to controls using the analysis of HRV in the domains of time, frequency, and nonlinearity, as well as analysis of the recurrence plots (RPs). Methods: We selected 34 patients with PFHH (29.4 ± 10.2 years) and 34 controls (29.2 ± 9.6 years) for HRV analysis. Heart beats were recorded with Polar RS800CX monitor (20 min, at rest, in supine position), and RR intervals were analyzed with Kubios Premium HRV software. RPs were constructed with Visual Recurrence Analysis software. Statistical analysis included unpaired t test (p < 0.05). Results: Our results showed that HRV parameters in the 3 domains evaluated did not show any differences between the groups. The same was observed with RPs. Conclusions: The findings suggest that PFHH, from the pathophysiological point of view, may be caused by peripheral involvement of the sympathetic nervous system (glandular level or nerve terminals), as there was no difference between the groups studied. More specific studies should help elucidate this issue.

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Quantitative assessment of autonomic nervous system responses induced by graded training workloads in healthy elderly, through indices obtained from heart rate variability analysis

European Heart Journal ◽

10.1093/eurheartj/ehab724.3045 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

R Fenici ◽

M Picerni ◽

D Brisinda

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Nervous System ◽

Autonomic Nervous System ◽

Quantitative Assessment ◽

Training Session ◽

Time Varying ◽

Autonomic Nervous ◽

Healthy Elderly ◽

Hrv Analysis

Abstract Background Quantitative assessment of individual body adaptability to physical training performed with the purposes of health maintenance is particularly necessary in the elderly age, to avoid the risk of overstrain induced by inappropriate exercises workload and physical stress. For that purpose, heart rate monitors and heart rate variability (HRV) analysis are nowadays commercially available. However, their reliability to guide individualized fitness training in elderly people needs to be tested, knowing that users might not have medical education. Objective To preliminary quantify autonomic nervous system (ANS) responses to graded physical effort and recovery in healthy elderly basing on the parasympathetic nervous system (PNSi), the sympathetic nervous system (SNSi) and the stress (STRi) indices, derived by short-term and time-varying HRV analysis. Methods ECG of a 75 healthy male subject was monitored, from April to November 2020, during three times/week training sessions with a professional bike–ergometer. Each session consisted of 10 minutes baseline rest, 5 minutes warm-up, 30 minutes work and 10 minutes recovery. According to age, the training workload was graded from low (65–75 watt/min), to moderate (75–85 watt/min), semi-intensive (85–95 watt/min) and intensive (95–110 watt/min). For this pilot study, ECG data of only 40 training sessions (10 sessions for each workload to evaluate reproducibility) were analyzed with Kubios Premium software (version 3.4.1), in the time (TD) and frequency (FD) domains, with nonlinear (NL) methods and with time-varying (TV) algorithms. Short-time HRV was calculated from 2-minutes intervals. The PNSi, SNSi and STRi induced by each workload were averaged and compared. Results Average values of PNSi, SNSi and STRi were significantly different (p<0.05) among training sessions carried out with different workloads (Table 1A) and among measurements obtained at rest, at every 5 minutes step of each 30 minutes training session, and at 1 and 5 minutes of recovery (Table 1B). Interestingly, the correlation between SNSi and STRi was strictly linear (R= 0,98), whereas that between PNSi and STRi was better fitted by a cubic function (R=0,82 with cubic vs 0.68 with linear function), when evaluated either as a function of the sessions' workloads (Figure 1A), or of four time-intervals of each training session (Figure 1B). PNSi and SNSi were inversely correlated, with cross-point at about 15 minutes of training and 75 watt/min workload. Conclusions The calculation of PNSi, SNSi and STRi from HRV analysis is an efficient method for quick and simplified quantitative assessment of dynamic ASN adaptation to effort-induced stress from HRV analysis. If confirmed, the method may be useful for safer and even remote monitoring of training/rehabilitation in elderly. However, more detailed evaluation of spectral and NL parameters may be necessary to interpret more complex patterns of abnormal cases. FUNDunding Acknowledgement Type of funding sources: None. Table 1 Figure 1

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Multiscale Fine-Grained Heart Rate Variability Analysis for Recognizing the Severity of Hypertension

Computational and Mathematical Methods in Medicine ◽

10.1155/2019/4936179 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Hongbo Ni ◽

Ying Wang ◽

Guoxing Xu ◽

Ziqiang Shao ◽

Wei Zhang ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Nervous System ◽

Autonomic Nervous System ◽

Good Method ◽

Heart Rate Variability Analysis ◽

Time Frequency ◽

Autonomic Nervous ◽

Fine Grained ◽

Multifactor Analysis

Hypertension is a common and chronic disease and causes severe damage to patients’ health. Blood pressure of a human being is controlled by the autonomic nervous system. Heart rate variability (HRV) is an impact of the autonomic nervous system and an indicator of the balance of the cardiac sympathetic nerve and vagus nerve. HRV is a good method to recognize the severity of hypertension due to the specificity for prediction. In this paper, we proposed a novel fine-grained HRV analysis method to enhance the precision of recognition. In order to analyze the HRV of the patient, we segment the overnight electrocardiogram (ECG) into various scales. 18 HRV multidimensional features in the time, frequency, and nonlinear domain are extracted, and then the temporal pyramid pooling method is designed to reduce feature dimensions. Multifactor analysis of variance (MANOVA) is applied to filter the related features and establish the hypertension recognizing model with relevant features to efficiently recognize the patients’ severity. In this paper, 139 hypertension patients’ real clinical ECG data are applied, and the overall precision is 95.1%. The experimental results validate the effectiveness and reliability of the proposed recognition method in the work.

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Heart Rate Variability Analysis in Patients with Allergic Rhinitis

The Scientific World JOURNAL ◽

10.1155/2013/947385 ◽

2013 ◽

Vol 2013 ◽

pp. 1-4 ◽

Cited By ~ 6

Author(s):

Ming-Ying Lan ◽

Guo-She Lee ◽

An-Suey Shiao ◽

Jen-Hung Ko ◽

Chih-Hung Shu

Keyword(s):

Heart Rate ◽

Allergic Rhinitis ◽

Heart Rate Variability ◽

Nervous System ◽

Low Frequency ◽

Sitting Position ◽

Rr Intervals ◽

Hrv Analysis ◽

Low Frequency Power ◽

Frequency Power

Background. Very few studies investigate the role of the autonomic nervous system in allergic rhinitis. In this study, we evaluated the autonomic nervous system in allergic rhinitis patients using heart rate variability (HRV) analysis.Methods. Eleven patients with allergic rhinitis and 13 healthy controls, aged between 19 and 40 years old, were enrolled in the study. Diagnosis of allergic rhinitis was based on clinical history, symptoms, and positive Phadiatop test. Electrocardiographic recordings on the sitting and supine positions were obtained for HRV analysis.Results. In the supine position, there were no significant statistical differences in very-low-frequency power (VLF, ≤0.04 Hz), low-frequency power (LF, 0.04–0.15 Hz), high-frequency power (HF, 0.15–0.40 Hz), and the ratio of LF to HF (LF/HF) between the patient and control groups. The mean RR intervals significantly increased, while LF% and LF/HF significantly decreased in the patient group in the sitting position. Moreover, mean RR intervals, LF, and LF/HF, which were significantly different between the two positions in the control group, did not show a significant change with the posture change in the patient group.Conclusion. These suggest that patients with allergic rhinitis may have poor sympathetic modulation in the sitting position. Autonomic dysfunction may therefore play a role in the pathophysiology of allergic rhinitis.

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Heart Rate Variability Analysis in Risk of Asthma Stratification

Jornada de Jóvenes Investigadores del I3A ◽

10.26754/jji-i3a.201711971 ◽

2017 ◽

Vol 5 ◽

Author(s):

Javier Milagro ◽

Eduardo Gil ◽

Jesús Lázaro ◽

Ville-Pekka Seppä ◽

L. Pekka Malmberg ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Nervous System ◽

Autonomic Nervous System ◽

Early Diagnosis ◽

Heart Rate Variability Analysis ◽

Long Term Effects ◽

Variability Analysis ◽

Hrv Analysis

Early diagnosis of asthma is crucial to avoid long-term effects such as permanent airway obstruction. Pathogenesis of asthma has been related with autonomic nervous system (ANS) dysfunction, concretely with abnormal parasympathetic activity. As heart rate variability (HRV) analysis does reflect ANS activity, it has been employed here in risk of asthma stratification.

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Stationary Bike Training Performance Using Heart Rate Variability

10.29007/j6zx ◽

2020 ◽

Author(s):

Quoc Cuong Pham ◽

Tran Duc Minh Nguyen ◽

Dang Le Cao ◽

Quoc Khai Le ◽

Quang Linh Huynh

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Training Performance ◽

Time Frequency ◽

Effective Training ◽

Hrv Analysis ◽

And Performance ◽

Training Exercises ◽

Response To Exercise ◽

Electrocardiogram Ecg

Exercising is said to bring benefits to people taking part in, not only physical but also physiological gain. Heart Rate Variability (HRV) is an important marker reflecting the function of the autonomic nervous system (ANS), which has shown potentials in some exercise therapy and sport physiology studies. HRV analysis is said to be used for getting a better understanding of our body’s response to exercise and the reaction to different stressors from the workout. Thus, it is essential to monitor and optimize the recovery to avoid overtraining. This study aims to investigate the influence of HRV reflecting the physical stress level on participants when exercising, therefore, building a concept of self-training guide to improve the adaptation and performance. Electrocardiogram (ECG) is acquired by the BIOPAC system over 10 healthy college students during a proposed training protocol on the stationary bike, and post-exercising. HRV data from ECG is analyzed in time, frequency and nonlinear domains to extract various features to evaluate physiological recovery status, manage physical fatigue, intensity adjustment. From the evaluation of these indexes, participants are able to keep track of their physiological condition as well as to have more effective training exercises.

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Estimation of Spectral Entropy of HRV Data and its Application to Depression and Thyroid Subjects to Predict Cardiac Risk

Biomedical & Pharmacology Journal ◽

10.13005/bpj/1532 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1643-1648

Author(s):

C. H. Renu Madhavi

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Nervous System ◽

Cardiac Dysfunction ◽

Healthy Subjects ◽

Cardiac Risk ◽

Sampling Frequency ◽

Spectral Entropy ◽

The Status ◽

Hrv Analysis

Heart Rate variability (HRV) analysis using nonlinear techniques is very much useful to estimate the status of Autonomic Nervous System (ANS) non invasively. In this paper ANS status is estimated using nonlinear Spectral Entropy (SE) technique for both Healthy, Depression and Thyroid subjects. HRV data of Thyroid , Depression and healthy subjects is derived from Power lab Instrument with sampling frequency of 500Hz.Three observations are made based on the estimated values of SE. First observation is that SE values of Thyroid and depression subjects is lower compared to Healthy subjects SE values. Second observation made is that among Thyroid and Depression subjects SE values are found to be the lowest for Thyroid subjects compared to Depression subjects. Third observation made is that the SE values of CHF and AF subjects is lower than Thyroid and depression subjects. From the three observations made lower values of SE indicates the existence of ANS dysfunction for AF,CHF, thyroid and depression subjects Further it indicates that these subjects are at higher risk of cardiac dysfunction as compared to CHF and AF subjects leading to mortality. From the results obtained it may be concluded that among the Thyroid and Depression subjects Thyroid subjects indicate higher ANS dysfunction indicating Thyroid subjects are at higher risk of cardiac dysfunction..These findings are in agreement with the medical findings available in the literature.

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Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience

Organizational Research Methods ◽

10.1177/1094428116681072 ◽

2016 ◽

Vol 22 (1) ◽

pp. 354-393 ◽

Cited By ~ 30

Author(s):

Sebastiano Massaro ◽

Leandro Pecchia

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Nervous System ◽

Ecological Validity ◽

Heart Rhythm ◽

Neuroscience Research ◽

Hrv Analysis ◽

Methodological Considerations ◽

The Brain ◽

Human Nervous System

Recently, the application of neuroscience methods and findings to the study of organizational phenomena has gained significant interest and converged in the emerging field of organizational neuroscience. Yet, this body of research has principally focused on the brain, often overlooking fuller analysis of the activities of the human nervous system and associated methods available to assess them. In this article, we aim to narrow this gap by reviewing heart rate variability (HRV) analysis, which is that set of methods assessing beat-to-beat changes in the heart rhythm over time, used to draw inference on the outflow of the autonomic nervous system (ANS). In addition to anatomo-physiological and detailed methodological considerations, we discuss related theoretical, ethical, and practical implications. Overall, we argue that this methodology offers the opportunity not only to inform on a wealth of constructs relevant for management inquiries but also to advance the overarching organizational neuroscience research agenda and its ecological validity.

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Embedded System Based on an ARM Microcontroller to Analyze Heart Rate Variability in Real Time Using Wavelets

Wireless Communications and Mobile Computing ◽

10.1155/2018/9138578 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14

Author(s):

Victor H. Rodriguez ◽

Carlos Medrano ◽

Inmaculada Plaza

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Embedded System ◽

Real Time ◽

Qrs Complex ◽

Local Networks ◽

Time Frequency ◽

Hrv Analysis ◽

Time Required ◽

Processing Techniques

The analyses of electrocardiogram (ECG) and heart rate variability (HRV) are of primordial interest for cardiovascular diseases. The algorithm used for the detection of the QRS complex is the basis for HRV analysis and HRV quality will depend strongly on it. The aim of this paper is to implement HRV analysis in real time on an ARM microcontroller (MCU). Thus, there is no need to send raw data to a cloud server for real time HRV monitoring and, consequently, the communication requirements and the power consumption of the local sensor node would be far lower. The system would facilitate the integration into edge computing, for instance, in small local networks, such as hospitals. A QRS detector based on wavelets is proposed, which is able to autonomously select the coefficients the QRS complex will be detected with. To validate it, the MITBIH and NSRDB databases were used. This detector was implemented in real time using an MCU. Subsequently HRV analysis was implemented in the time, frequency, and nonlinear domains. When evaluating the QRS detector with the MITBIH database, 99.61% positive prediction (PP), 99.3% sensitivity (SE), and a prediction error rate (DER) of 1.12% were obtained. For the NSRDB database the results were a PP of 99.95%, an SE of 99.98%, and a DER of 0.0006%. The execution of the QRS detector in the MCU took 52 milliseconds. On the other hand, the time required to calculate the HRV depends on the data size, but it took only a few seconds to analyze several thousands of interbeat intervals. The results obtained for the detector were superior to 99%, so it is expected that the HRV is reliable. It has also been shown that the detection of QRS complex can be done in real time using advanced processing techniques such as wavelets.

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