Estimation of Heart Rate Variability Changes during Different Visual Stimulations Using Non-invasive Continuous ECG Monitoring System

2009 ◽  
Author(s):  
Wanqing Wu ◽  
Jungtae Lee ◽  
Haifeng Chen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Monitoring System ◽  
Ecg Monitoring ◽  
Non Invasive

Design of heart rate variability processor for portable 3-lead ECG monitoring system-on-chip

2013 ◽  
Vol 40 (5) ◽  
pp. 1491-1504 ◽  
Author(s):  
Wai-Chi Fang ◽  
Hsiang-Cheh Huang ◽  
Shao-Yen Tseng
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Monitoring System ◽  
System On Chip ◽  
Ecg Monitoring ◽  
On Chip

scholarly journals A wearable system for real-time outpatient ECG monitoring

2019 ◽  
pp. 26-30
Author(s):  
T.O. Білобородова ◽  
І.С. Скарга-Бандурова ◽  
В.С. Дерев’янченко
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Monitoring System ◽  
Morphological Feature ◽  
Well Being ◽  
Wearable Devices ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Systems Architecture

Functional state of the cardiovascular system is an important factor for human physical well-being. To perform analysis of the cardiovascular state, the wearable continuous ECG monitoring system is essential. In this paper, a wearable ECG monitoring system based on IoT is proposed. The systems architecture is presented. Wearable devices design employs few optimal components for the acquisition of acceptable ECG signal. The R peaks corresponding to each heartbeat, and T waves, a morphological feature of the ECG are detected. It enables to perform heart rate and heart rate variability analyses, as well as  extract, store and analyze  the long term ECG measurements.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

Trends in Heart Rate and Heart Rate Variability during Pregnancy and Three Months Postpartum: Continuous Monitoring in a Free-Living Context (Preprint)

2021 ◽  
Author(s):  
Fatemeh Sarhaddi ◽  
Iman Azimi ◽  
Anna Axelin ◽  
Hannakaisa Niela-Vilen ◽  
Pasi Liljeberg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pregnant Women ◽  
Time Domain ◽  
Second Trimester ◽  
Third Trimester ◽  
Non Invasive ◽  
The Third ◽  
Invasive Method ◽  
Frequency Power

BACKGROUND Heart rate variability (HRV) is a non-invasive method reflecting autonomic nervous system (ANS) regulations. Altered HRV is associated with adverse mental or physical health complications. ANS also has a central role in physiological adaption during pregnancy causing normal changes in HRV. OBJECTIVE Assessing trends in heart rate (HR) and HRV parameters as a non-invasive method for remote maternal health monitoring during pregnancy and three months postpartum. METHODS Fifty-eight pregnant women were monitored using an Internet-of-Things (IoT)-based remote monitoring system during pregnancy and 3-months postpartum. Pregnant women were asked to continuously wear Gear sport smartwatch to monitor their HR and HRV. In addition, a cross-platform mobile application was utilized for collecting pregnancy-related information. The trends of HR and HRV parameters were extracted using reliable data. We also analyzed the trends of normalized HRV parameters based on HR to remove the effect of HR changes on HRV trends. Finally, we exploited hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters. RESULTS HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P<.01). Time-domain HRV parameters, average normal interbeat intervals (AVNN), standard deviation of normal interbeat intervals (SDNN), root mean square of the successive difference of normal interbeat intervals (RMSSD), normalized SDNN (nSDNN), and normalized RMSSD (nRMSSD) decreased significantly during the second trimester (P<.001) then increased significantly during the third trimester (P<.01). Some of the frequency domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF (nHF) decreased significantly during the second trimester (P<.01), and HF increased significantly during the third trimester (P<.01). In the postpartum period, nRMSSD decreased (P<.05), and the LF to HF ratio (LF/HF) increased significantly (P<.01). CONCLUSIONS Our study showed that HR increased and HRV parameters decreased as the pregnancy proceeded, and the values returned to normal after the delivery. Moreover, our results show that HR started to decrease while time-domain HRV parameters and HF started to increase during the third trimester. Our results also demonstrate the possibility of continuous HRV monitoring in everyday life settings.

HEART RATE VARIABILITY RELATED TO N95 RESPIRATOR USE IN INTERNS DURING COVID-19 PANDEMIC

2021 ◽  
pp. 69-70
Author(s):  
Pakanati Sujana ◽  
Venkata Mahesh Gandhavalla ◽  
K. Prabhakara Rao
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Low Frequency ◽  
Protective Equipment ◽  
Short Term ◽  
Non Invasive ◽  
Critical Components ◽  
The Individual ◽  
N95 Respirators ◽  
Respiratory Droplets

Introduction: COVID19 is caused by SARS-CoV-2 which is primarily transmitted through respiratory droplets and contact routes. WHO recommended the use of personal protective equipment (PPE) for prevention and N95 respirators are critical components of PPE. Breathing through N95 respirator will impart stress in the individual and that can be assessed by heart rate variability (HRV). HRV measures the variation in time between each heartbeat controlled by autonomic nervous system (ANS), which is a non invasive reliable index to identify the ANS imbalances. Aims And Objectives: This study is aimed at assessing the HRV of Interns working in COVID19 wards using N95 respirators. Methodology: This study included 100 interns in whom short term HRV was recorded using the standard protocol. Lead II of ECG was recorded using AD instruments (ADI) 8channel polygraph and HRV was analysed using Labchart 8pro software. The recordings were taken before and 1hour after wearing N95 respirator. Results: Overall HRV (SDRR) was found to decrease signicantly after wearing N95 respirator for 1hr (p=0.000). Similarly, indices representing the parasympathetic component ( RMSSD and HF ) were also found to decrease signicantly with the use of N95 respirator. Low frequency (LF) power and LF/HF ratio increased signicantly with N95 respirator use (p=0.000). Conclusion: We conclude that using N95 respirator increased sympathetic activity reecting decreased HRV in our subjects Hence we recommend that it is better to change the duty pattern for interns.

Personalized mathematical model of endotoxin-induced inflammatory responses in young men and associated changes in heart rate variability

2018 ◽  
Vol 13 (5) ◽  
pp. 42 ◽  
Author(s):  
R. Brady ◽  
D.O. Frank-Ito ◽  
H.T. Tran ◽  
S. Janum ◽  
K. Møller ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Inflammatory Responses ◽  
Interleukin 10 ◽  
Tnf Alpha ◽  
Non Invasive ◽  
Estimated Parameters ◽  
Subject Specific ◽  
The Subject ◽  
Necrosis Factor

The objective of this study was to develop a personalized inflammatory model and estimate subject-specific parameters that could be related to changes in heart rate variability (HRV), a measure that can be obtained non-invasively in real time. An inflammatory model was developed and calibrated to measurements of interleukin-6 (IL-6), tumor necrosis factor (TNF-alpha), interleukin-8 (IL-8) and interleukin-10 (IL-10) over 8 hours in 20 subjects administered a low dose of lipopolysaccharide. For this model, we estimated 11 subject-specific parameters for all 20 subjects. Estimated parameters were correlated with changes in HRV, computed from ECG measurements using a built-in HRV module available in Labchart. Results revealed that patients could be separated into two groups expressing normal and abnormal responses to endotoxin. Abnormal responders exhibited increased HRV, most likely as a result of increased vagal firing. The observed correlation between the inflammatory response and HRV brings us a step further towards understanding if HRV predictions can be used as a marker for inflammation. Analyzing HRV parameters provides an easy, non-invasively obtained measure that can be used to assess the state of the subject, potentially translating to identifying a non-invasive marker that can be used to detect the onset of sepsis.

scholarly journals Does non-invasive vagus nerve stimulation affect heart rate variability? A living and interactive Bayesian meta-analysis

2021 ◽  
Author(s):  
Vinzent Wolf ◽  
Anne Kühnel ◽  
Vanessa Teckentrup ◽  
Julian Koenig ◽  
Nils B. Kroemer
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vagus Nerve ◽  
Brain Stimulation ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Meta Analysis ◽  
Current Evidence ◽  
Beneficial Effects ◽  
Non Invasive

AbstractNon-invasive brain stimulation techniques, such as transcutaneous auricular vagus nerve stimulation (taVNS), have considerable potential for clinical use. Beneficial effects of taVNS have been demonstrated on symptoms in patients with mental or neurological disorders as well as transdiagnostic dimensions, including mood and motivation. However, since taVNS research is still an emerging field, the underlying neurophysiological processes are not yet fully understood, and the replicability of findings on biomarkers of taVNS effects has been questioned. Here, we perform a living Bayesian random effects meta-analysis to synthesize the current evidence concerning the effects of taVNS on heart rate variability (HRV), a candidate biomarker that has, so far, received most attention in the field. To keep the synthesis of evidence transparent and up to date as new studies are being published, we developed a Shiny web app that regularly incorporates new results and enables users to modify study selection criteria to evaluate the robustness of the inference across potential confounds. Our analysis focuses on 17 single-blind studies comparing taVNS versus sham in healthy participants. These newly synthesized results provide strong evidence for the null hypothesis (g = 0.011, CIshortest = [−0.103, 0.125], BF01 = 25.587), indicating that acute taVNS does not alter HRV compared to sham. To conclude, based on a synthesis of the available evidence to date, there is no support for the hypothesis that HRV is a robust biomarker for acute taVNS. By increasing transparency and timeliness, we believe that the concept of living meta-analyses can lead to transformational benefits in emerging fields such as non-invasive brain stimulation.

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