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Artykuły w czasopismach na temat „HRV analysis”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 3 lutego 2022

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1

Savolainen, Carita, Pia Laine, Mick N. Mulders, and Tapani Hovi. "Sequence analysis of human rhinoviruses in the RNA-dependent RNA polymerase coding region reveals large within-species variation." Journal of General Virology 85, no. 8 (August 1, 2004): 2271–77. http://dx.doi.org/10.1099/vir.0.79897-0.

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Human rhinoviruses (HRVs; family Picornaviridae), the most frequent causative agents of respiratory infections, comprise more than 100 distinct serotypes. According to previous phylogenetic analysis of the VP4/VP2-coding sequences, all but one of the HRV prototype strains distribute between the two established species, Human rhinovirus A (HRV-A) and Human rhinovirus B (HRV-B). Here, partial sequences of the RNA-dependent RNA polymerase (3D polymerase)-coding gene of 48 HRV prototype strains and 12 field isolates were analysed. The designated division of the HRV strains into the species HRV-A a

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Laine, Pia, Carita Savolainen, Soile Blomqvist, and Tapani Hovi. "Phylogenetic analysis of human rhinovirus capsid protein VP1 and 2A protease coding sequences confirms shared genus-like relationships with human enteroviruses." Journal of General Virology 86, no. 3 (March 1, 2005): 697–706. http://dx.doi.org/10.1099/vir.0.80445-0.

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Phylogenetic analysis of the capsid protein VP1 coding sequences of all 101 human rhinovirus (HRV) prototype strains revealed two major genetic clusters, similar to that of the previously reported VP4/VP2 coding sequences, representing the established two species, Human rhinovirus A (HRV-A) and Human rhinovirus B (HRV-B). Pairwise nucleotide identities varied from 61 to 98 % within and from 46 to 55 % between the two HRV species. Interserotypic sequence identities in both HRV species were more variable than those within any Human enterovirus (HEV) species in the same family. This means that un

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Koch, Celine, Marcel Wilhelm, Stefan Salzmann, Winfried Rief, and Frank Euteneuer. "A meta-analysis of heart rate variability in major depression." Psychological Medicine 49, no. 12 (June 26, 2019): 1948–57. http://dx.doi.org/10.1017/s0033291719001351.

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AbstractBackgroundMajor depression (MD) is a risk factor for cardiovascular disease. Reduced heart rate variability (HRV) has been observed in MD. Given the predictive value of HRV for cardiovascular health, reduced HRV might be one physiological factor that mediates this association.MethodsThe purpose of this study was to provide up-to-date random-effects meta-analyses of studies which compare resting-state measures of HRV between unmedicated adults with MD and controls. Database search considered English and German literature to July 2018.ResultsA total of 21 studies including 2250 patients

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Blasi, Anna, Javier Jo, Edwin Valladares, Barbara J. Morgan, James B. Skatrud, and Michael C. K. Khoo. "Cardiovascular variability after arousal from sleep: time-varying spectral analysis." Journal of Applied Physiology 95, no. 4 (October 2003): 1394–404. http://dx.doi.org/10.1152/japplphysiol.01095.2002.

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We performed time-varying spectral analyses of heart rate variability (HRV) and blood pressure variability (BPV) recorded from 16 normal humans during acoustically induced arousals from sleep. Time-varying autoregressive modeling was employed to estimate the time courses of high-frequency HRV power, low-frequency HRV power, the ratio between low-frequency and high-frequency HRV power, and low-frequency power of systolic BPV. To delineate the influence of respiration on HRV, we also computed respiratory airflow high-frequency power, the modified ratio of low-frequency to high-frequency HRV powe

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5

Schneider, Martha, and Andreas Schwerdtfeger. "Autonomic dysfunction in posttraumatic stress disorder indexed by heart rate variability: a meta-analysis." Psychological Medicine 50, no. 12 (August 28, 2020): 1937–48. http://dx.doi.org/10.1017/s003329172000207x.

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AbstractBackgroundChanges in autonomic nervous system (ANS) function have been observed in a variety of psychological disorders, including posttraumatic stress disorder (PTSD). Analysis of heart rate variability (HRV) provides insight into the functioning of the ANS. Previous research on PTSD found lower HRV in PTSD patients compared to controls, indicating altered sympathetic and parasympathetic activity, but findings are inconsistent. The purpose of this meta-analysis was to examine differences in HRV indices between individuals with PTSD and healthy controls at baseline and during stress.Me

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Gawade, Ratnadeep. "ECG Analysis for HRV Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 2283–86. http://dx.doi.org/10.22214/ijraset.2021.34137.

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In this paper an algorithm is proposed for estimation of HRV with better accuracy and results. We are making use of Auto Regressive Model (AR Model) for the estimation. Since ECG wave is also contaminated with a lot of noise such as Power Line Interference (PLI), EMG and just some common artifacts like breathing disturbance’s, so to filter out all this noise from the wave we are using Cumulant based AR model for filtering the wave. Using IoT we will later use real time ECG waves to estimate HRV.

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German-Sallo, Zoltan. "Wavelet Transform based HRV Analysis." Procedia Technology 12 (2014): 105–11. http://dx.doi.org/10.1016/j.protcy.2013.12.462.

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Niskanen, Juha-Pekka, Mika P. Tarvainen, Perttu O. Ranta-aho, and Pasi A. Karjalainen. "Software for advanced HRV analysis." Computer Methods and Programs in Biomedicine 76, no. 1 (October 2004): 73–81. http://dx.doi.org/10.1016/j.cmpb.2004.03.004.

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Sieciński, Szymon, Paweł S. Kostka, and Ewaryst J. Tkacz. "Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers." Sensors 20, no. 16 (August 13, 2020): 4522. http://dx.doi.org/10.3390/s20164522.

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Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolb

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10

McIntyre, Chloe L., E. Carol McWilliam Leitch, Carita Savolainen-Kopra, Tapani Hovi, and Peter Simmonds. "Analysis of Genetic Diversity and Sites of Recombination in Human Rhinovirus Species C." Journal of Virology 84, no. 19 (July 28, 2010): 10297–310. http://dx.doi.org/10.1128/jvi.00962-10.

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ABSTRACT Human rhinoviruses (HRVs) are a highly prevalent and diverse group of respiratory viruses. Although HRV-A and HRV-B are traditionally detected by virus isolation, a series of unculturable HRV variants have recently been described and assigned as a new species (HRV-C) within the picornavirus Enterovirus genus. To investigate their genetic diversity and occurrence of recombination, we have performed comprehensive phylogenetic analysis of sequences from the 5′ untranslated region (5′ UTR), VP4/VP2, VP1, and 3Dpol regions amplified from 89 HRV-C-positive respiratory samples and available

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11

Lee, Dae-Young, and Young-Seok Choi. "Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability." Entropy 20, no. 12 (December 11, 2018): 952. http://dx.doi.org/10.3390/e20120952.

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Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel

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12

Sheridan, David C., Ryan Dehart, Amber Lin, Michael Sabbaj, and Steven D. Baker. "Heart Rate Variability Analysis: How Much Artifact Can We Remove?" Psychiatry Investigation 17, no. 9 (September 25, 2020): 960–65. http://dx.doi.org/10.30773/pi.2020.0168.

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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 (NCT0303

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13

Matteucci, M., L. Mainardi, and V. D. Corino. "Analysis of Heart Rate Variability to Predict Patient Age in a Healthy Population." Methods of Information in Medicine 46, no. 02 (2007): 191–95. http://dx.doi.org/10.1055/s-0038-1625405.

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Summary Objectives : To estimate age of healthy subjects by means of the heart rate variability (HRV) parameters thus assessing the potentiality of HRV indexes as a biomarker of age. Methods : Long-term indexes of HRV in time domain, frequency domain and non-linear parameters were computed on 24-hour recordings in a dataset of 63 healthy subjects (age range 20-76 years old). Then, as interbeat dynamics markedly change with age, showing a reduced HRV in older subjects, we tried to capture age-related influence on HRV by principal component analysis and to predict the subject age by means of a f

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14

Zhao, Lina, Peng Li, Jianqing Li, and Chengyu Liu. "Influence of Ectopic Beats on Heart Rate Variability Analysis." Entropy 23, no. 6 (May 22, 2021): 648. http://dx.doi.org/10.3390/e23060648.

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The analysis of heart rate variability (HRV) plays a dominant role in the study of physiological signal variability. HRV reflects the information of the adjustment of sympathetic and parasympathetic nerves on the cardiovascular system and, thus, is widely used to evaluate the functional status of the cardiovascular system. Ectopic beats may affect the analysis of HRV. However, the quantitative relationship between the burden of ectopic beats and HRV indices, including entropy measures, has not yet been investigated in depth. In this work, we analyzed the effects of different numbers of ectopic

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15

Pham, Tam, Zen Juen Lau, S. H. Annabel Chen, and Dominique Makowski. "Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial." Sensors 21, no. 12 (June 9, 2021): 3998. http://dx.doi.org/10.3390/s21123998.

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The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reli

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Tripathi, Kirthi, Harsh Sohal, and Shruti Jain. "Statistical Analysis of HRV Parameters for the Detection of Arrhythmia." International Journal of Image and Graphics 20, no. 04 (October 2020): 2050036. http://dx.doi.org/10.1142/s0219467820500369.

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The repolarization and depolarization in heart generate electrical signals in the form of an ECG wave. The condition of the heart can be indicated by using Heart Rate Variability (HRV) features. In this work, FIR filter is used at the pre-processing phase for denoising, and then statistical analysis is applied for time-domain HRV feature extraction and selection. This algorithm is evaluated on different records of MIT/BIH Normal Sinus Rhythm and Arrhythmia database. The [Formula: see text]-test implementation in both databases shows that there are significant variations in HRV features, where

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Pereira-Junior, Pedro P., Moacir Marocolo, Fabricio P. Rodrigues, Emiliano Medei, and José H. M. Nascimento. "Noninvasive method for electrocardiogram recording in conscious rats: feasibility for heart rate variability analysis." Anais da Academia Brasileira de Ciências 82, no. 2 (June 2010): 431–37. http://dx.doi.org/10.1590/s0001-37652010000200019.

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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,

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Toledo, Eran, Osnat Gurevitz, Hanoch Hod, Michael Eldar, and Solange Akselrod. "Wavelet analysis of instantaneous heart rate: a study of autonomic control during thrombolysis." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 284, no. 4 (April 1, 2003): R1079—R1091. http://dx.doi.org/10.1152/ajpregu.00287.2002.

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Myocardial infarction (MI) is known to elicit activation of the autonomic nervous system. Reperfusion, induced by thrombolysis, is thus expected to bring about a shift in the balance between the sympathetic and vagal systems, according to the infarct location. In this study, we explored the correlation between reperfusion and the spectral components of heart rate (HR) variability (HRV), which are associated with autonomic cardiac control. We analyzed the HR of patients during thrombolysis: nine anterior wall MI (AW-MI) and eight inferoposterior wall MI (IW-MI). Reperfusion was determined from

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Antali, Flóra, Dániel Kulin, Konrád István Lucz, Balázs Szabó, László Szűcs, Sándor Kulin, and Zsuzsanna Miklós. "Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System." Sensors 21, no. 16 (August 18, 2021): 5544. http://dx.doi.org/10.3390/s21165544.

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Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We u

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Lee, Myeong Soo, Young Hoon Rim, Dong-Myong Jeong, Mo Kyung Kim, Min Cheol Joo, and Sun Ho Shin. "Nonlinear Analysis of Heart Rate Variability During Qi Therapy (External Qigong)." American Journal of Chinese Medicine 33, no. 04 (January 2005): 579–88. http://dx.doi.org/10.1142/s0192415x05003181.

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Heart rate variability (HRV) was compared in 30 subjects receiving external Qi therapy (EQT) or placebo control therapy, in a crossover design experiment. Subjects who received the EQT reported more pleasant and calm emotions than did the placebo group. Qi therapy reduced the heart rate and increased HRV as indicated by a reduced low frequency/high frequency power ratio of HRV. With nonlinear analysis, the Poincaré plot index of HRV and approximate entropy was greater in the EQT group than in the control group. These findings suggest that EQT stabilizes the sympathovagal function and cardiac a

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Markovics, Zigurds, Juris Lauznis, Matiss Erins, Olesja Minejeva, and Raivis Kivlenieks. "Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 1211. http://dx.doi.org/10.14419/ijet.v7i4.36.28191.

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The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and

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Markovics, Zigurds, Juris Lauznis, Matiss Erins, Olesja Minejeva, and Raivis Kivlenieks. "Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 1211. http://dx.doi.org/10.14419/ijet.v7i4.36.28214.

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The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and

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Germán-Salló, Zoltan, and Márta Germán-Salló. "Non-linear Methods in HRV Analysis." Procedia Technology 22 (2016): 645–51. http://dx.doi.org/10.1016/j.protcy.2016.01.134.

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Shi, Bo, Mohammod Abdul Motin, Xinpei Wang, Chandan Karmakar, and Peng Li. "Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series." Entropy 22, no. 12 (December 20, 2020): 1439. http://dx.doi.org/10.3390/e22121439.

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QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, i

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Zamaklar-Trifunovic, Danijela, Petar Seferovic, Milan Petrovic, Mirjana Zivkovic, Goran Vukomanovic, Natasa Milic, Arsen Ristic, and Marija Zdravkovic. "The influence of respiratory pattern on heart rate variability analysis in heart failure." Srpski arhiv za celokupno lekarstvo 135, no. 3-4 (2007): 135–42. http://dx.doi.org/10.2298/sarh0704135z.

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Introduction. Autonomic dysfunction is present early in the course of heart failure, and has a direct role on deterioration of cardiac function and prognosis. Heart rate variability (HRV) estimates sympathovagal control of heart frequency. The influence of respiratory pattern on HRV is clinically important. Breathing disorders are common in heart failure and highly affect HRV and autonomic evaluation. It was previously shown that slow and deep breathing increased parasympathetic tone, but effects of this respiratory pattern on HRV were not evaluated. Objective. The aim of the study was to esti

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Jirkovská, Alexandra, Petr Boucek, Stephanie Wu, Jana Hosová, Robert Bém, Vladimira Fejfarova, and Jelena Skibová. "Power Spectral Analysis of Heart Rate Variability in Patients with Charcot’s Neuroarthropathy." Journal of the American Podiatric Medical Association 96, no. 1 (January 1, 2006): 1–8. http://dx.doi.org/10.7547/0960001.

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Charcot’s or neuropathic osteoarthropathy is one of the most debilitating orthopedic sequelae of diabetes mellitus. Distinguishing Charcot’s neuroarthropathy from clinically similar conditions may be challenging. The neurovascular theory postulates that Charcot’s neuroarthropathy may be secondary to sympathetic denervation of the lower-extremity vasculature. A convenient method for assessing autonomic neuropathy in patients with Charcot’s neuroarthropathy is needed. Short-term power spectral analysis (PSA) of heart rate variability (HRV), a noninvasive and quantitative method for assessing aut

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DUA, SUMEET, XIAN DU, S. VINITHA SREE, and THAJUDIN AHAMED V. I. "NOVEL CLASSIFICATION OF CORONARY ARTERY DISEASE USING HEART RATE VARIABILITY ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240017. http://dx.doi.org/10.1142/s0219519412400179.

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Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced significantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended fluctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample ent

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Xu, Jianbo, and Wenxi Chen. "Impact of Water Temperature on Heart Rate Variability during Bathing." Life 11, no. 5 (April 22, 2021): 378. http://dx.doi.org/10.3390/life11050378.

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Background: Heart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing. Methods: The bathtub WT was preset at three conditions: i.e., low WT (36–38 °C), medium WT (38–40 °C), and high WT (40–42 °C), respectively. Ten subjects participated in the data collection. Each subject collected five electrocardiogram (ECG) recordings at each preset bathtub WT condition. Each recording was 18 min long with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. Twenty HRV features we

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Koenig, Julian, DeWayne P. Williams, Andrew H. Kemp, and Julian F. Thayer. "Vagally mediated heart rate variability in headache patients—a systematic review and meta-analysis." Cephalalgia 36, no. 3 (May 11, 2015): 265–78. http://dx.doi.org/10.1177/0333102415583989.

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Objective Vagal nerve activity—indexed by heart rate variability (HRV)—has been linked to altered pain processing and inflammation, both of which may underpin headache disorders and lead to cardiovascular disease (CVD). Here we examined the evidence for differences in parasympathetic (vagal) activity indexed by time- and frequency-domain measures of HRV in patients with headache disorders compared to healthy controls (HCs). Methods A systematic review and meta-analysis was conducted on studies investigating group differences in vagally mediated HRV (vmHRV) including time- (root-mean-square of

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Blaber, A. P., R. L. Bondar, and R. Freeman. "Coarse graining spectral analysis of HR and BP variability in patients with autonomic failure." American Journal of Physiology-Heart and Circulatory Physiology 271, no. 4 (October 1, 1996): H1555—H1564. http://dx.doi.org/10.1152/ajpheart.1996.271.4.h1555.

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We examined heart rate and blood pressure variability (HRV and BPV) during graded tilt (5 min in each position: supine, -10 degrees, 10 degrees, 30 degrees, 60 degrees, -10 degrees, supine) in autonomic failure patients and age-matched controls. Heart rate was not different between patients and controls and increased with tilt (P < 0.001). Total HRV was reduced in patients (P < 0.03). Patients had reduced low-frequency (0-0.15 Hz) HRV and BPV (P < 0.005). With tilt, low-frequency BPV increased in controls, whereas high-frequency (> 0.15 Hz) BPV increased in patients. The slope of t

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T, Vyasaraj, and Veena N. Hegde. "Heart Rate Variability-A Review." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 26, 2021): 1241–46. http://dx.doi.org/10.51201/jusst/21/07296.

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Information about the health can be obtained by measuring various physiological parameters such as Heart Rate, Heart Rate Variability (HRV), Nerve conduction, brain activity, blood oxygen saturation level etc. The useful information resulted from the systematic analysis of these physical parameters are helpful for clinicians to make better decisions. HRV reflects the state of the Autonomic Nervous System (ANS) defined as the variance in the time between successive heartbeats expressed in milliseconds. The various factors that affects HRV are diet, nutrition, age, alcohol, gender, cardiac rhyth

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Tomes, Colin, Ben Schram, and Robin Orr. "Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis." Sustainability 13, no. 14 (July 15, 2021): 7895. http://dx.doi.org/10.3390/su13147895.

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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

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Bánhalmi, András, János Borbás, Márta Fidrich, Vilmos Bilicki, Zoltán Gingl, and László Rudas. "Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera." Journal of Healthcare Engineering 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/4038034.

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Background. Heart rate variability (HRV) provides information about the activity of the autonomic nervous system. Because of the small amount of data collected, the importance of HRV has not yet been proven in clinical practice. To collect population-level data, smartphone applications leveraging photoplethysmography (PPG) and some medical knowledge could provide the means for it. Objective. To assess the capabilities of our smartphone application, we compared PPG (pulse rate variability (PRV)) with ECG (HRV). To have a baseline, we also compared the differences among ECG channels. Method. We

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Chen, C. L., H. H. Lin, William C. Orr, Cheryl C. H. Yang, and Terry B. J. Kuo. "Transfer function analysis of heart rate variability in response to water intake: correlation with gastric myoelectrical activity." Journal of Applied Physiology 96, no. 6 (June 2004): 2226–30. http://dx.doi.org/10.1152/japplphysiol.01037.2003.

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We utilized transfer function analysis of heart rate variability (HRV) and respiration to investigate the effect of water intake on gastric myoelectrical activity and its relationship to vagal activity. The electrogastrography (EGG) and HRV were recorded simultaneously before and after drinking 500 ml of water in 10 healthy subjects. We observed good linearity between lung volumes and HRV signals at a ventilatory rate between 0.2 and 0.4 Hz before and after water intake. The EGG power of 3 cycles/min increased remarkably after the water intake. We found that there was a significant increase in

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Clamor, Annika, Tania M. Lincoln, Julian F. Thayer, and Julian Koenig. "Resting vagal activity in schizophrenia: Meta-analysis of heart rate variability as a potential endophenotype." British Journal of Psychiatry 208, no. 1 (January 2016): 9–16. http://dx.doi.org/10.1192/bjp.bp.114.160762.

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BackgroundCardiac vagal tone, indexed by heart rate variability (HRV), is a proxy for the functional integrity of feedback mechanisms integrating central and peripheral physiology.AimsTo quantify differences in HRV in individuals with schizophrenia compared with healthy controls.MethodDatabases were systematically searched for studies eligible for inclusion. Random effect meta-analyses of standardised mean differences were calculated for vagal activity indicated by high-frequency HRV and the root mean square of successive R–R interval differences (RMSSD).ResultsThirty-four studies were include

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Capdevila, Lluis, Jesús Castro-Marrero, José Alegre, Juan Ramos-Castro, and Rosa M. Escorihuela. "Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology." Sensors 21, no. 11 (May 28, 2021): 3746. http://dx.doi.org/10.3390/s21113746.

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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-repo

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Gehlen, Heidrun, Johanna Loschelder, Roswitha Merle, and Maike Walther. "Evaluation of Stress Response under a Standard Euthanasia Protocol in Horses Using Analysis of Heart Rate Variability." Animals 10, no. 3 (March 13, 2020): 485. http://dx.doi.org/10.3390/ani10030485.

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The effects of a standard protocol for euthanasia on heart rate variability (HRV) as a consequence of stress response were analyzed in this prospective clinical study. The HRV was determined in 40 horses undergoing euthanasia due to various reasons, at different locations, and with/without owner presence. For euthanasia, horses were sedated with xylazine or a combination of xylazine and butorphanol. General anesthesia was induced using diazepam and ketamine. Afterwards, horses were euthanized with pentobarbital. The ECG data were taken by a Telemetric ECG at three time points (sedation, anesth

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Ni, Hongbo, Ying Wang, Guoxing Xu, Ziqiang Shao, Wei Zhang, and Xingshe Zhou. "Multiscale Fine-Grained Heart Rate Variability Analysis for Recognizing the Severity of Hypertension." Computational and Mathematical Methods in Medicine 2019 (January 22, 2019): 1–9. http://dx.doi.org/10.1155/2019/4936179.

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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 over

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Uusitalo, A. L. T., E. Vanninen, E. Levälahti, M. C. Battié, T. Videman, and J. Kaprio. "Role of genetic and environmental influences on heart rate variability in middle-aged men." American Journal of Physiology-Heart and Circulatory Physiology 293, no. 2 (August 2007): H1013—H1022. http://dx.doi.org/10.1152/ajpheart.00475.2006.

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Our aim was to estimate causal relationships of genetic factors and different specific environmental factors in determination of the level of cardiac autonomic modulation, i.e., heart rate variability (HRV), in healthy male twins and male twins with chronic diseases. The subjects were 208 monozygotic (MZ, 104 healthy) and 296 dizygotic (DZ, 173 healthy) male twins. A structured interview was used to obtain data on lifetime exposures of occupational loading, regularly performed leisure-time sport activities, coffee consumption, smoking history, and chronic diseases from 12 yr of age through the

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Kobayashi, Hiromitsu, Chorong Song, Harumi Ikei, Takahide Kagawa, and Yoshifumi Miyazaki. "Analysis of Individual Variations in Autonomic Responses to Urban and Forest Environments." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/671094.

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Autonomic responses to urban and forest environments were studied in 625 young male subjects. The experimental sites were 57 forests and 57 urban areas across Japan. The subjects viewed the landscape (forest or urban environment) for a period of 15 min while sitting on a chair. During this period, heart rate variability (HRV) was monitored continuously. The results were presented as histograms and analyzed with special reference to individual variations. Approximately 80% of the subjects showed an increase in the parasympathetic indicator of HRV (lnHF), whereas the remaining subjects showed a

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Shinba, Toshikazu, Keizo Murotsu, Yosuke Usui, Yoshinori Andow, Hiroshi Terada, Nobutoshi Kariya, Yoshitaka Tatebayashi, et al. "Return-to-Work Screening by Linear Discriminant Analysis of Heart Rate Variability Indices in Depressed Subjects." Sensors 21, no. 15 (July 30, 2021): 5177. http://dx.doi.org/10.3390/s21155177.

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Using a linear discriminant analysis of heart rate variability (HRV) indices, the present study sought to verify the usefulness of autonomic measurement in major depressive disorder (MDD) patients by assessing the feasibility of their return to work after sick leave. When reinstatement was scheduled, patients’ HRV was measured using a wearable electrocardiogram device. The outcome of the reinstatement was evaluated at one month after returning to work. HRV indices including high- and low-frequency components were calculated in three conditions within a session: initial rest, mental task, and r

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SREE, S. VINITHA, DHANJOO N. GHISTA, and KWAN-HOONG NG. "CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 05 (December 2012): 1240032. http://dx.doi.org/10.1142/s0219519412400325.

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An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classi

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Grant, C., P. J. Becker, and M. Viljoen. "Determining heart rate variation: a comparison between direct results from V6 of the ECG and time domain, frequency domain and non-liniar analysis." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 25, no. 2 (September 22, 2006): 67–81. http://dx.doi.org/10.4102/satnt.v25i2.148.

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Oscillation around a mean value is found in the intervals between consecutive heartbeats (R-R intervals). This oscillation is known as heart rate variability (HRV). Various methods can be used to estimate HRV, but the extent to which agreement exists between the results obtained by the different methods is not known. In this study HRV, as determined directly from the ECG, is compared to results obtained by time domain analysis, frequency domain analysis, Poincaré graphs and fractal analysis. Results showed that, in individuals with low HRV values, the results obtained by time domain analysis,

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Martín-Montero, Adrián, Gonzalo C. Gutiérrez-Tobal, David Gozal, Verónica Barroso-García, Daniel Álvarez, Félix del Campo, Leila Kheirandish-Gozal, and Roberto Hornero. "Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea." Entropy 23, no. 8 (August 6, 2021): 1016. http://dx.doi.org/10.3390/e23081016.

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Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0–13 years). The first database (training set) was composed of 981 overnight

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Kirti, Kirti, Harsh Sohal, and Shruti Jain. "Comparative Analysis of Heart Rate Variability Parameters for Arrhythmia and Atrial Fibrillation using ANOVA." Biomedical and Pharmacology Journal 11, no. 4 (December 25, 2018): 1841–49. http://dx.doi.org/10.13005/bpj/1556.

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Heart Rate Variability (HRV) is an important criterion to check the cardiac health. Sudden HRV signifies the unhealthy condition of the heart, particularly when the person is suffering from a cardiac disease. HRV parameters on different patients of different ages, gender and health conditions are observed using time domain, geometrical domain and frequency domain. Statistical comparison is done on three different databases MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) using Analysis of Variance (ANOVA) technique. We have extracted twenty HRV fe

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DESAI, KAMALAKAR, DHANJOO N. GHISTA, ISSAM JAHA EL MUGAMEX, U. RAJENDRA ACHARYA, MICHAEL TOWSEY, SULTAN ABDUL ALI, MOHAMMED SAEED, and M. AMIN FIKRI. "DIABETIC AUTONOMIC NEUROPATHY DETECTION BY HEART-RATE VARIABILITY POWER-SPECTRAL ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 03 (June 2012): 1250039. http://dx.doi.org/10.1142/s0219519411004794.

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Heart rate is a non-stationary signal and provides a powerful interplay between the sympathetic and parasympathetic nervous systems. The heart rate variation signal can reveal disorders associated with how these nervous systems regulate the heart rate, and hence may contain indicators of this disease state, or warnings about impending or future cardiac diseases. These indicators may be present at all times or may occur at random during certain intervals in the time scale. It is difficult and time consuming to pinpoint these abnormalities in a huge cardiac data set. Heart rate variability (HRV)

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Ledford, Rebecca M., Nitesh R. Patel, Tina M. Demenczuk, Adiba Watanyar, Torsten Herbertz, Marc S. Collett, and Daniel C. Pevear. "VP1 Sequencing of All Human Rhinovirus Serotypes:Insights into Genus Phylogeny and Susceptibility to AntiviralCapsid-BindingCompounds." Journal of Virology 78, no. 7 (April 1, 2004): 3663–74. http://dx.doi.org/10.1128/jvi.78.7.3663-3674.2004.

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ABSTRACT Rhinoviruses are the most common infectious agents of humans. They are the principal etiologic agents of afebrile viral upper-respiratory-tract infections (the common cold). Human rhinoviruses (HRVs) comprise a genus within the family Picornaviridae. There are >100 serotypically distinct members of this genus. In order to better understand their phylogenetic relationship, the nucleotide sequence for the major surface protein of the virus capsid, VP1, was determined for all known HRV serotypes and one untyped isolate (HRV-Hanks). Phylogenetic analysis of deduced amino acid sequence

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Lu, Wan-An, Jui-Feng Lin, Chen-Hsu Wang, Yung-Sheng Chen, Ying-Hua Shieh, and Cheng-Deng Kuo. "Cross-Spectral Analysis of Electrocardiographic and Nostril Airflow Signals Identifies Two Respiratory Frequencies of Heart Rate Modulation." Journal of Healthcare Engineering 2021 (January 24, 2021): 1–7. http://dx.doi.org/10.1155/2021/6636829.

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Respiration is known to be a significant modulator of heart rate, and the high-frequency component in the power spectrum of heart rate variability (HRV) is believed to be caused mainly by respiration. To investigate the effect of respiration on heart rate, cross-spectral analysis of electrocardiographic (ECG) and nostril airflow signals was performed in healthy subjects to find the common features of ECG and respiration. Forty-two healthy subjects were included in this study. The autospectra of respective ECG and nostril airflow signals and the cross-spectra of ECG and nostril airflow signals

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Tsuji, Hiroyuki, and Hirohiko Mori. "New analysis of HRV through wavelet transform." International Journal of Human-Computer Interaction 6, no. 2 (April 1994): 205–17. http://dx.doi.org/10.1080/10447319409526091.

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Badilini, Fabio, and Pierre Maison Blanche. "HRV Spectral Analysis by the Averaged Periodogram." Annals of Noninvasive Electrocardiology 1, no. 4 (October 1996): 423–29. http://dx.doi.org/10.1111/j.1542-474x.1996.tb00300.x.

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