Journal articles on the topic 'HRV analysis'
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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 and HRV-B was also seen in the 3D-coding region. Phylogenetically, HRV-B clustered closer to human enterovirus (HEV) species HEV-B, HEV-C and poliovirus than to HRV-A. Intraspecies variation within both HRV-A and HRV-B was greater in the 3D-coding region than in the VP4/VP2-coding region, with the difference maxima reaching 48 % at the nucleotide level and 36 % at the amino acid level in HRV-A and 53 and 35 %, respectively, in HRV-B. Within both species, a few strains formed a separate cluster differing from the majority of strains as much as HEV-B from HEV-C. Furthermore, the tree topology within HRV-A differed from that for VP4/VP2, suggesting possible recombination events in the evolutionary history of the strains. However, all 12 field isolates clustered similarly, as in the capsid region. These results showed that the within-species variation in the 3D region is greater in HRV than in HEV. Furthermore, HRV variation in the 3D region exceeds that in the capsid-coding region.
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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 unequivocal serotype identification by VP1 sequence analysis used for HEV strains may not always be possible for HRV isolates. On the other hand, a comprehensive insight into the relationships between VP1 and partial 2A sequences of HRV and HEV revealed a genus-like situation. Distribution of pairwise nucleotide identity values between these genera varied from 41 to 54 % in the VP1 coding region, similar to those between heterologous members of the two HRV species. Alignment of the deduced amino acid sequences revealed more fully conserved amino acid residues between HRV-B and polioviruses than between the two HRV species. In phylogenetic trees, where all HRVs and representatives from all HEV species were included, the two HRV species did not cluster together but behaved like members of the same genus as the HEVs. In conclusion, from a phylogenetic point of view, there are no good reasons to keep these two human picornavirus genera taxonomically separated.
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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 and 1982 controls were extracted. Significant differences between patients and controls were found for (i) frequency domains such as HF-HRV [Hedges' g = −0.318; 95% CI (−0.388 to −0.247)], LF-HRV (Hedges' g = −0.195; 95% CI (−0.332 to −0.059)], LF/HF-HRV (Hedges' g = 0.195; 95% CI (0.086–0.303)] and VLF-HRV (Hedges' g = −0.096; 95% CI (−0.179 to −0.013)), and for (ii) time-domains such as IBI (Hedges' g = −0.163; 95% CI (−0.304 to −0.022)], RMSSD (Hedges' g = −0.462; 95% CI (−0.612 to −0.312)] and SDNN (Hedges' g = −0.266; 95% CI (−0.431 to −0.100)].ConclusionsOur findings demonstrate that all HRV-measures were lower in MD than in healthy controls and thus strengthens evidence for lower HRV as a potential cardiovascular risk factor in these 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 power, and the average transfer gain between respiration and heart rate. During cortical arousal, muscle sympathetic nerve activity and heart rate increased and returned rapidly to baseline, but systolic blood pressure, the ratio between low-frequency and high-frequency HRV power, low-frequency HRV power, the modified ratio of low-frequency to high-frequency HRV power, and low-frequency power of systolic BPV displayed increases that remained above baseline up to 40 s after arousal. High-frequency HRV power and airflow high-frequency power showed concommitant decreases to levels below baseline, whereas the average transfer gain between respiration and heart rate remained unchanged. These findings suggest that 1) arousal-induced changes in parasympathetic activity are strongly coupled to respiratory pattern and 2) the sympathoexcitatory cardiovascular effects of arousal are relatively long lasting and may accumulate if repetitive arousals occur in close succession.
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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.MethodsThe included primary studies present an aggregate of studies analyzing different HRV indices. Examined HRV indices were standard deviation of the normalized NN-intervals (SDNN), root mean square of successive differences (RMSSD), low-frequency (LF) and high-frequency (HF) spectral components, LF/HF ratio, and heart rate (HR). Moderating effects of study design, HRV and PTSD assessment, and sample characteristics were examined via subgroup-analyses and meta-regressions.ResultsRandom-effects meta-analyses for HRV parameters at rest revealed significant group differences for RMSSD and HF-HRV, suggesting lower parasympathetic activity in PTSD. The aggregated effect size for SDNN was medium, suggesting diminished total variability in PTSD. A small effect was found for LF-HRV. A higher LF/HF ratio was found in the PTSD sample as compared to controls. Individuals with PTSD showed significantly higher HR. During stress, individuals with PTSD showed higher HR and lower HF-HRV, both indicated by small effect sizes.ConclusionsFindings suggest that PTSD is associated with ANS dysfunction.
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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 Toolbox. The heartbeats in ECG were detected using the Pan–Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.
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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 published sequences. Branching orders of VP4/VP2, VP1, and 3Dpol trees were identical, consistent with the absence of intraspecies recombination in the coding regions. However, numerous tree topology changes were apparent in the 5′ UTR, where >60% of analyzed HRV-C variants showed recombination with species A sequences. Two recombination hot spots in stem-loop 5 and the polypyrimidine tract in the 5′ UTR were mapped using the program GroupingScan. Available HRV-C sequences showed evidence for additional interspecies recombination with HRV-A in the 2A gene, with breakpoints mapping precisely to the boundaries of the C-terminal domain of the encoded proteinase. Pairwise distances between HRV-C variants in VP1 and VP4/VP2 regions fell into two separate distributions, resembling inter- and intraserotype distances of species A and B. These observations suggest that, without serological cross-neutralization data, HRV-C genetic groups may be equivalently classified into types using divergence thresholds derived from distance distributions. The extensive sequence data from multiple genome regions of HRV-C and analyses of recombination in the current study will assist future formulation of consensus criteria for HRV-C type assignment and identification.
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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 multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.
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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 (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.
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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 feedforward neural network. Results : The network provides good prediction of patient age, even if a slight overestimation in the younger subjects and a slight underestimation in the older ones were observed. In addition, the important contribution of non-linear indexes to prediction is underlined. Conclusions : HRV as a predictor of age may lead to the definition of a new biomarker of aging.
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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 beats on several widely accepted HRV parameters in time-domain (SDNN), frequency-domain (LF/HF), as well as non-linear features (SampEn and Pt-SampEn (physical threshold-based SampEn)). The results showed that all four indices were influenced by ectopic beats, and the degree of influence was roughly increased with the increase of the number of ectopic beats. Ectopic beats had the greatest impact on the frequency domain index LF/HF, whereas the Pt-SampEn was minimally accepted by ectopic beats. These results also indicated that, compared with the other three indices, Pt-SampEn had better robustness for ectopic beats.
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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 reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.
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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 meanRR and HR have suggestive significant ([Formula: see text]) changes, while maxRR, minRR, maxminRR, and SDNN have strongly significant ([Formula: see text]) changes. To validate the statistical analysis of HRV, feature classification has been done using SVM and kNN classifiers. A significant improvement of 2% and 14.02% has been observed in the overall accuracy of SVM and kNN classifiers after feature selection, respectively. These HRV features can be used for the early prediction of various Cardio-Vascular Diseases (CVD).
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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, 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.
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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 changes in ST levels and reported pain. Reocclusion was detected in four patients. HRV was analyzed using a modified continuous wavelet transform, which provided time-dependent versions of the typically used low-frequency (LF) and high-frequency (HF) peaks and of their ratio, LF/HF. Marked alterations in at least one of the HRV parameters was found in all 18 reperfusion events. Patterns of HRV, compatible with a shift toward relative sympathetic enhancement, were found in all of the nine reperfusion events in IW-MI patients and in three AW-MI patients. Patterns of HRV compatible with relative vagal enhancement were found in six AW-MI patients ( P < 0.001). Significant changes in HRV parameters were also found after reocclusion. Time-dependent spectral analysis of HRV using the wavelet transform was found to be valuable for explaining the patterns of cardiac rate control during reperfusion. In addition, examination of the entire record revealed epochs of markedly diminished HRV in two patients, which we attribute to vagal saturation.
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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 used Bland–Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters.
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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 autonomic nervous system by inducing more positive emotions than the placebo therapy. In conclusion, EQT may act by stabilizing both the autonomic nervous system and the emotional state.
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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 HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
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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 HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
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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, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.
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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 estimate effects of slow and deep breathing (SDB) on HRV in heart failure patients. Method. In 55 patients with heart failure (78% male, mean age 57.18?10.8 yrs, mean EF=34.12?10.01%) and 14 healthy controls (57.1% male, mean age 53.1?8.2 yrs), short term HRV spectral analysis was performed (Cardiovit AT 60, Schiller). VLF, LF, HF and LF/HF were determined during spontaneous and deep and slow breathing at 0.1 Hz (SDB). Results. LF, HF and LF/HF significantly increased during SDB compared with spontaneous breathing both in controls (LF 50.71?61.55 vs. 551.14?698.01 ms2, p<0.001; HF 31.42?29.98 vs.188.78?142.74 ms2, p<0.001 and LF/HF 1.46?0.61 vs. 4.21?3.23, p=0.025) and heart failure patients (LF 27.37?36.04 vs. 94.50?96.13 ms2, p<0.001; HF 12.13?19.75 vs. 41.58?64.02 ms2, p<0.001 and LF/HF 3.77?3.79 vs. 6.38?5.98, p=0.031). Increments of LF and HF induced by SDB were significantly lower in patients than healthy controls. Heart failure patients had lower HRV compared to healthy controls both during spontaneous breathing and SDB. During spontaneous breathing, only HF was significantly lower between healthy controls and patients (p=0.002). During SDB VLF (p=0.022), LF (p<0.001) and HF (p<0.001) were significantly lower in heart failure patients compared to controls. Conclusion. These data suggest that SDB increases HRV both in healthy and heart failure patients; the highest increment is in LF range. Differences in spectral profile of HRV between healthy controls and heart failure patients become more profound during SDB. Controlled respiration during HRV analysis might increase sensitivity and reliability in detection of autonomic dysfunction in heart failure patients. .
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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 autonomic neuropathy, may be advantageous compared with the traditionally used Ewing’s cardiovascular reflex tests. However, there are limitations to the clinical use of PSA of HRV because of poor standardization. We standardized PSA of HRV and assessed autonomic neuropathy in 17 people with acute Charcot’s neuroarthropathy using PSA of HRV versus Ewing’s tests. More patients with Charcot’s neuroarthropathy were diagnosed as having autonomic neuropathy with PSA of HRV than with Ewing’s tests (94% versus 82%); however, no significant difference between the two methods was found. The results of this study suggest that PSA of HRV requires minimal patient collaboration and time expenditure compared with Ewing’s tests and may be useful in detecting autonomic neuropathy in patients with Charcot’s neuroarthropathy. (J Am Podiatr Med Assoc 96(1): 1–8, 2006)
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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 entropy (SampEn). These features were subjected to principal component analysis (PCA). The significant principal components were evaluated using eight classification techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classification accuracy (89.5%) using our proposed technique.
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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 were calculated using 1-min ECG segments each time. The k-means clustering analysis method was used to analyze the rough trends based on the preset WT. Analyses of the significant differences were performed using the multivariate analysis of variance of t-tests, and the mean and standard deviation (SD) of each HRV feature based on the WT were calculated. Results: The statistics show that with increasing WT, 11 HRV features are significantly (p < 0.05) and monotonously reduced, four HRV features are significantly (p < 0.05) and monotonously rising, two HRV features are rising first and then reduced, two HRV features (fuzzy and approximate entropy) are almost unchanged, and vLF power is rising. Conclusion: The WT has an important impact on HRV during bathing. The findings in the present work reveal an important physiological factor that affects the dynamic changes of HRV and contribute to better quantitative analyses of HRV in future research works.
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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 successive R-R-interval differences (RMSSD)) and frequency- (high-frequency HRV) domain measures. Studies eligible for inclusion were identified by a systematic search of the literature, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Results Seven studies reporting a total of 10 comparisons of patients with headache disorders (HF-HRV n = 67, RMSSD n = 122) and HCs (HF-HRV n = 64, RMSSD n = 125) were eligible for inclusion. Random-effects meta-analysis revealed a significant main effect on RMSSD ( Z = 2.03, p = 0.04; Hedges’ g = −0.63; 95% CI (−1.24, –0.02); k = 6) and similar pooled effect size estimates for HF-HRV when breathing was controlled ( g = −0.30; 95% CI (−0.69; 0.10)) but not when breathing was not controlled ( g = 0.02; 95% CI (−0.69; 0.74)). Controlling for breathing had no effect on RMSSD. Conclusion vmHRV is reduced in patients with headache disorders, findings associated with a medium effect size. Suggestions for future research in this area are provided, emphasizing a need to investigate the impact of headache disorders and commonly comorbid conditions—including mental disorders—as well as the investigation of the risk for CVD in migraine in particular. We further emphasize the need for large-scale studies to investigate HRV as a mechanism mediating the association of migraine and CVD.
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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 the fractal component (beta) for HRV and BPV was not different between patients and controls. HRV-beta increased (1.5-1.9, P < 0.01) with tilt, but BPV-beta (approximately 1.8) was unaffected. Values of beta close to 1 indicate high signal regulatory complexity, and values of beta close to 2 indicate low complexity. HRV and BPV provide clear evidence of impaired sympathetic and parasympathetic autonomic nervous system response to tilt with autonomic failure. The similarity in signal complexity with reduced fractal and harmonic spectral power, in patients compared with controls, suggests unchanged cardiovascular neural input and integration with reduced output in autonomic failure.
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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 rhythm, sleep habits, genetics etc. The analysis of HRV is helpful in stress assessment and also in identifying the diseases at the early stage. This paper discusses the fundamentals of HRV, analysis of HRV, and the role of HRV in stress detection.
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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 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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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 took fifty parallel measurements using iPhone 6 at a 240 Hz sampling frequency and Cardiax PC-ECG devices. The correspondence between the PRV and HRV indices was investigated using correlation, linear regression, and Bland-Altman analysis. Results. High PPG accuracy: the deviation of PPG-ECG is comparable to that of ECG channels. Mean deviation between PPG-ECG and two ECG channels: RR: 0.01 ms–0.06 ms, SDNN: 0.78 ms–0.46 ms, RMSSD: 1.79 ms–1.21 ms, and pNN50: 2.43%–1.63%. Conclusions. Our iPhone application yielded good results on PPG-based PRV indices compared to ECG-based HRV indices and to differences among ECG channels. We plan to extend our results on the PPG-ECG correspondence with a deeper analysis of the different ECG channels.
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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 the magnitude of the respiration-HRV transfer function after water intake ( P < 0.05). The EGG 3 cycles/min power was positively correlated with the transfer magnitude throughout the study ( r = 0.54, P = 0.01). These results confirm that transfer function analysis of HRV sensitively identifies subtle changes in the respiratory sinus arrhythmia that occurs with water intake. The present findings suggest that transfer function analysis of HRV and respiration after water intake can be used to evaluate vagal nervous activity in the human gut.
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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 included. Significant main effects were found for high-frequency HRV (P = 0.0008; Hedges' g =–0.98, 95% CI −1.56 to −0.41, k = 29) and RMSSD (P<0.0001; g =–0.91, 95% CI −1.19 to −0.62, k = 24), indicating lower vagal activity in individuals with schizophrenia than in healthy controls. Considerable heterogeneity was evident but effects were robust in subsequent sensitivity analyses.ConclusionsGiven the association between low HRV, threat processing, emotion regulation and executive functioning, reduced vagal tone may be an endophenotype for the development of psychotic symptoms.
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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-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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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, anesthesia, anesthesia until death). The HRV was analyzed including the low (LF) and high frequency (HF) components of HRV and the sympathovagal balance (LF/HF ratio). Significant differences in the LF, HF and LF/HF ratio were found between the three time points of euthanasia (p < 0.001). The HRV analysis showed dominating sympathetic activity in the preparation phase of euthanasia and during the injection of pentobarbital. The location of euthanasia, presence of owner and type of primary diseases had no influence on stress parameters. Horses showing excitations or groaning during euthanasia did not differ in HRV. Horse with colic were however more likely to show reoccurrence of breathing during euthanasia. In conclusion, HRV is a sensitive, noninvasive parameter to obtain sympathovagal stimulations during euthanasia and adapted protocols for euthanasia in horse with colic should be studied.
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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 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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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 present. A 5-min ECG at supine rest was recorded for the HRV analyses. In univariate statistical analyses based on genetic models with additive genetic, dominance genetic, and unique environmental effects, genetic effects accounted for 31–57% of HRV variance. In multivariate statistical analysis, body mass index, percent body fat, coffee consumption, smoking, medication, and chronic diseases were associated with different HRV variables, accounting for 1–11% of their variance. Occupational physical loading and leisure-time sport activities did not account for variation in any HRV variable. However, in the subgroup analysis of healthy and diseased twins, occupational loading explained 4% of the variability in heart periods. Otherwise, the interaction between health status and genetic effects was significant for only two HRV variables. In conclusion, genetic factors accounted for a major portion of the interindividual differences in HRV, with no remarkable effect of health status. No single behavioral determinant appeared to have a major influence on HRV. The effects of medication and diseases may mask the minimal effect of occupational loading on HRV.
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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 decrease in the parasympathetic activity. Similarly, 64.0% of the subjects exhibited decreases in the sympathetic indicator of HRV (ln[LF/HF]), whereas the remaining subjects showed opposite responses. Analysis of the distribution of HRV indices (lnHF and ln[LF/HF]) demonstrated the effect of forest environments on autonomic activity more specifically than the conventional analysis based on the difference in mean values.
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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 rest after task. A linear discriminant function was made using the HRV indices of 30 MDD patients from our previous study to effectively discriminate the successful reinstatement from the unsuccessful reinstatement; this was then tested on 52 patients who participated in the present study. The discriminant function showed that the sensitivity and specificity in discriminating successful from unsuccessful returns were 95.8% and 35.7%, respectively. Sensitivity is high, indicating that normal HRV is required for a successful return, and that the discriminant analysis of HRV indices is useful for return-to-work screening in MDD patients. On the other hand, specificity is low, suggesting that other factors may also affect the outcome of reinstatement.
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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 classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.
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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, frequency domain analysis, Poincaré graphs and alpha 1 of fractal analysis are comparable to those calculated directly from the ECG. However, time domain analysis, frequency domain analysis, Poincaré graphs and fractal analysis are less sensitive than direct ECG measurements in individuals with higher HRV.
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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 ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0–0.04 Hz; low frequency: 0.04–0.15 Hz; and high frequency: 0.15–0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001–0.005 Hz; BW2: 0.028–0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.
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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 features from all the three domains, which show weak, moderate or strong significant changes as per the relation during comparison with respective databases. Out of twenty only nine features are selected which shows noticeable difference between three databases. Later, the selected features will be used for classification in future.
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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) constitutes a tool for assessing the activities of the autonomic nervous system (ANS). In this work, we have proposed a computer based analytical system to determine the HRV, and analyzed it to obtain HRV Power-spectrum for normal, diabetes and diabetes with neuropathy subjects in deep breathing, standing and supine position. We have then designated indices based on the HRV power-spectra power values and frequency shift of these peaks from their normal frequency values. We have shown the efficacy and sensitivity of these indices, to differentiate between normals, diabetics and diabetics with ischemic heart disease. Thus we have demonstrated how effectively these HRV power-spectral indices can enable diagnosis of diabetic autonomic neuropathy. Finally, we have composed an integrated index made up of these power-spectral indices, to facilitate distinguishing and diagnosing diabetic autonomic neuropathy in terms of just one index or number.
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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 data support previous studies subdividing the genus into two species containing all but one HRV serotype (HRV-87). Seventy-five HRV serotypes and HRV-Hanks belong to species HRV-A, and twenty-five HRV serotypes belong to species HRV-B. Located within VP1 is a hydrophobic pocket into which small-molecule antiviral compounds such as pleconaril bind and inhibit functions associated with the virus capsid. Analyses of the amino acids that constitute this pocket indicate that the sequence correlates strongly with virus susceptibility to pleconaril inhibition. Further, amino acid changes observed in reduced susceptibility variant viruses recovered from patients enrolled in clinical trials with pleconaril were distinct from those that confer natural phenotypic resistance to the drug. These observations suggest that it is possible to differentiate rhinoviruses naturally resistant to capsid function inhibitors from those that emerge from susceptible virus populations as a result of antiviral drug selection pressure based on sequence analysis of the drug-binding pocket.
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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 were obtained and compared with the corresponding conventional HRV measures. We found that there were two spectral peaks at around 0.03 Hz and 0.3 Hz in the autospectrum of nostril airflow and the cross-spectrum of ECG and nostril airflow. In addition, the cross-spectral normalized high-frequency power (nHFPcs) was significantly larger than that of conventional HRV, while the cross-spectral normalized very low-frequency power (nVLFPcs), normalized low-frequency power (nLFPcs), and low-/high-frequency power ratio (LHRcs) were significantly lower than those of the conventional HRV. The cross-spectral nLFPcs and LHRcs had positive correlations with their corresponding HRV measures. We conclude that cross-spectral analysis of ECG and nostril airflow signals identifies two respiratory frequencies at around 0.03 Hz and below and around 0.3 Hz and can yield significantly enhanced nHFPcs and significantly suppressed nVLFPcs, as compared to their counterparts in conventional HRV. Both very low-frequency and high-frequency components of HRV are caused in part or mainly by respiration.
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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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