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33 results for “Heart rate data”
Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'
<p>Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Data for "Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?"
<p>Abstract</p> <p>Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.</p> <p>Methods</p> <p>Subjects walked and ran on a classic Quinton 18-60 motorized treadmill with a rigid steel deck (Quinton Instrument Company, Bothell, WA).</p> <p><strong>Determination of PTS: </strong>The average of the walk-to-run transition speed (WRTS) and run-to-walk transition speed (RWTS) defined the PTS as per Hreljac et. al. (2007). We first determined the WRTS in the walk-first group and then their RWTS and <em>vice versa</em> for the run-first group. Based on pilot experiments, we selected starting speeds such that there was no doubt which gait would be preferred at the initial speed. Once the speed of the treadmill was correctly set, subjects mounted the treadmill and chose their gait <em>ad libitum</em>. After we determined the preferred gait at the particular speed, the subject straddled the treadmill belt while we changed the speed by 0.1 m/s (increased during WRTS trials, decreased during RWTS trials). The process repeated until a gait transition occurred and was sustained for 30 seconds.</p> <p><strong>Determination of EOTS and HROTS: </strong>For the energetics and heart rate trials, we set the initial speed based on pilot experiments that indicated it would be near the EOTS. Subjects in the walk-first group walked at the incline-specific initial speed for 5 min, rested for ∼5 min and then ran at that speed for 5 min. Subjects in the run-first group did the opposite. During the rest periods, we re-weighed the subject and they drank just enough water to compensate for the weight loss due mostly to sweating. Thus, each subject maintained a nearly constant weight throughout all the trials.</p> <p>To measure metabolic rate during walking and running, we used an open-circuit, expired gas analysis system (TrueOne 2400; ParvoMedics, Sandy, UT). Subjects wore a mouthpiece with a one-way breathing valve and a nose clip allowing us to collect their expired air. The ParvoMedics software calculated the STPD rates of oxygen consumption (V□O<sub>2</sub>) and carbon dioxide production (V□CO<sub>2</sub>) and we averaged the last 2 minutes of each 5-minute trial. We then calculated metabolic power using the equation of Péronnet and Massicotte (1991) equation, as clarified by Kipp et al. (2018). We only included trials with respiratory exchange ratios (RER) <1.0 to ensure that metabolic energy was predominantly being provided from oxidative pathways. We used an R7 Polar iWL (Polar Electro Oy, Kempele, Finland) to measure heart rate in beats per minute (bpm) and averaged the values for the last 2 min of each trial.</p> <p>Immediately after both gait trials were completed for the initial speed, we calculated and compared the metabolic power required for walking and running. If walking was the more economical gait, we increased the treadmill speed by 0.1 m/s, and the process repeated. If running was the more economical gait, we decreased the treadmill speed by 0.1 m/s, and the process repeated. Each subject performed three speeds, both walking and running at each incline. However, some subjects needed to complete walking and running trials at a fourth speed so that we could obtain energetics data for one speed faster and one speed slower than their EOTS.</p> <p>For the three speeds at which the differences between metabolic rates between walking and running were least, we calculated linear regression equations for both metabolic power and heart rate as functions of speed for both walking and running for each subject and incline. The speeds at which the two equations intersected defined the EOTS and HROTS for each subject.</p> <p>Overall, we analyzed ten subjects at four different inclines, i.e. 40 determinations of EOTS and HROTS. Of those 80 linear regression analyses, the walking vs. running regressions intersected at a speed < 3 m/sec for all but two subjects (one subject for EOTS at 15° and a different subject for HROTS at 10°). Essentially, those individuals’ regression lines were nearly parallel. We chose to exclude those two conditions from further statistical analysis and aggregate data compilation.</p> <p>Usage Notes</p> <p>There are two missing values, as noted in the methods: HROTS for subject 5 at 10 degrees and EOTS for subject 4 at 15 degrees.</p>
Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"
<p>Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019). </p> <p> </p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </p>
Data for: Non-invasive measurements of respiration and heart rate across wildlife species using Eulerian Video Magnification of infrared thermal imagery
<p><strong>Background</strong>: An animal's metabolic rate, or energetic expenditure, both impacts and is impacted by interactions with its environment. However, techniques for obtaining measurements of metabolic rate are invasive, logistically difficult, and costly. Red-green-blue (RGB) imaging tools have been used in humans and select domestic mammals to accurately measure heart and respiration rate, as proxies of metabolic rate. The purpose of this study was to investigate if infrared thermography (IRT) coupled with Eulerian video magnification (EVM) would extend the applicability of imaging tools towards measuring vital rates in exotic wildlife species with different physical attributes.</p> <p><strong>Results</strong>: We collected IRT and RGB video of 52 total species (39 mammalian, 7 avian, 6 reptilian) from 36 taxonomic families at zoological institutions and used EVM to amplify subtle changes in temperature associated with blood flow for respiration and heart rate measurements. IRT-derived respiration and heart rates were compared to 'true' measurements determined simultaneously by expansion of the ribcage/nostrils and stethoscope readings, respectively. Sufficient temporal signals were extracted for measures of respiration rate in 36 species (85% success in mammals; 50% success in birds; 100% success in reptiles) and heart rate in 24 species (67% success in mammals; 33% success in birds; 0% success in reptiles) using IRT-EVM. Infrared-derived measurements were obtained with high accuracy (respiration rate, mean absolute error: 1.9 breaths per minute, average percent error: 4.4%; heart rate, mean absolute error: 2.6 beats per minute, average percent error: 1.3%). Thick integument and animal movement most significantly hindered successful validation.</p> <p><strong>Conclusion</strong>: The combination of IRT with EVM analysis provides a non-invasive method to assess individual animal health in zoos, with great potential to monitor wildlife metabolic indices in situ.</p>
Data from: Effects of disbudding on behavior and heart rate during jugular venipuncture in dairy calves
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Data for: Non-invasive measurements of respiration and heart rate across wildlife species using Eulerian Video Magnification of infrared thermal imagery
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Data from: Body temperature, heart rate, and activity patterns of two boreal homeotherms in winter: homeostasis, allostasis, and ecological coexistence
<ol> <li>Organisms survive environmental variation by combining homeostatic regulation of critical states with allostatic variation of other traits, and species differences in these responses can contribute to coexistence in temporally-variable environments.</li> <li>In this paper, we simultaneously record variation in three functional traits – body temperature (Tb), heart rate, and activity - in relation to three forms of environmental variation – air temperature (Ta), photoperiod, and experimentally-manipulated resource levels – in free-ranging snowshoe hares and North American red squirrels to characterize distinctions in homeotherm responses to the extreme conditions of northern boreal winters.</li> <li>Hares and squirrels differed in the level and precision of Tb regulation, but also in the allostatic pathways necessary to maintain thermal homeostasis. Hares demonstrated a stronger metabolic pathway (through heart rate variation reflective of the thermogenesis), while squirrels demonstrated a stronger behavioral pathway (through activity variation that minimizes cold exposure).</li> <li>As intermediate-sized, winter-active homeotherms, hares and squirrels share many functional attributes, yet, through the integrated monitoring of multiple functional traits in response to shared environmental variation, our study reveals many pairwise species differences in homeostatic and allostatic traits, that both define and are defined by the natural history, functional niches, and coexistence of sympatric species.</li> </ol>
Data from: Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD's shocks are also very painful and affect a patient's quality of life. It would be ideal if ICDs can accurately predict the occurrence of ventricular tachyarrhythmia and then issue a warning or provide preventive therapy. Our study explores the use of ICD data to automatically predict ventricular arrhythmia using heart rate variability (HRV). A 5 minute and a 10 second warning system are both developed and compared. The participants for this study consist of 788 patients who were enrolled in the ICD arm of the Sudden Cardiac Death – Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed and different HRV features are extracted. Machine learning algorithms, including random forests (RF) and support vector machines (SVM), are trained on these features to classify the two groups of rhythms in a subset of the data comprising the training set. These algorithms are then used to classify rhythms in a separate test set. This performance is quantified by the area under the curve (AUC) of the ROC curve. Both RF and SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean AUC of 0.87-0.88 for 10-second prediction; an AUC over 0.8 typically warrants further clinical investigation. Our work shows that moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data. These results provide a realistic view of the practical challenges facing implementation of machine learning algorithms to predict ventricular tachyarrhythmia using HRV data, motivating continued research on improved algorithms and additional features with higher predictive power.
Data from: A 45-second self-test for cardiorespiratory fitness: heart rate-based estimation in healthy individuals
Cardio-respiratory fitness (CRF) is a widespread essential indicator in Sports Science as well as in Sports Medicine. This study aimed to develop and validate a prediction model for CRF based on a 45 second self-test, which can be conducted anywhere. Criterion validity, test re-test study was set up to accomplish our objectives. Data from 81 healthy volunteers (age: 29 ± 8 years, BMI: 24.0 ± 2.9), 18 of whom females, were used to validate this test against gold standard. Nineteen volunteers repeated this test twice in order to evaluate its repeatability. CRF estimation models were developed using heart rate (HR) features extracted from the resting, exercise, and the recovery phase. The most predictive HR feature was the intercept of the linear equation fitting the HR values during the recovery phase normalized for the height2 (r2 = 0.30). The Ruffier-Dickson Index (RDI), which was originally developed for this squat test, showed a negative significant correlation with CRF (r = -0.40), but explained only 15% of the variability in CRF. A multivariate model based on RDI and sex, age and height increased the explained variability up to 53% with a cross validation (CV) error of 0.532 L ∙ min-1 and substantial repeatability (ICC = 0.91). The best predictive multivariate model made use of the linear intercept of HR at the beginning of the recovery normalized for height2 and age2; this had an adjusted r2 = 0. 59, a CV error of 0.495 L·min-1 and substantial repeatability (ICC = 0.93). It also had a higher agreement in classifying CRF levels (κ = 0.42) than RDI-based model (κ = 0.29). In conclusion, this simple 45 s self-test can be used to estimate and classify CRF in healthy individuals with moderate accuracy and large repeatability when HR recovery features are included.
Data from: Measuring embryonic heart rate of wild birds: an opportunity to take the pulse on early development
Embryonic heart rate has the potential to provide great insight into physiological variation and ontogenic status in early development. The availability of a relatively inexpensive and portable piece of equipment – the Buddy egg monitor (Vetronic Services, UK), provides the opportunity to measure embryonic heart rate non-invasively in the field. Here we demonstrate the application of this equipment in the climatically harsh Australian outback. We characterize variation in embryonic heart rate in the zebra finch with respect to a range of abiotic and biotic variables. Heart rate increased throughout embryonic development and was positively correlated with ambient temperature. There was a strong effect of the nest of origin but no clear effect of laying order, or egg size, on embryonic heart rate. Our results demonstrate the sensitivity of embryonic heart rate to environmental conditions, and/or natal origin. We review studies that have used the digital egg monitor, and in discussing our own results identify areas of avian biology that could benefit from embryonic heart rate measurements in the future.
Data from: Cold-hearted bats: uncoupling of heart rate and metabolism during torpor at subzero temperatures
Many hibernating animals thermoregulate during torpor and defend their body temperature (Tb) below 10°C by an increase in metabolic rate. Above a critical temperature (Tcrit) animals usually thermoconform. We investigated the physiological responses above and below Tcrit for a small tree dwelling bat (Chalinolobus gouldii, ~14 g) that is often exposed to subzero temperatures during winter. Through simultaneous measurement of heart rate (HR) and oxygen consumption (V̇O2) we show that the relationship between oxygen transport and cardiac function is substantially altered in thermoregulating torpid bats between 1 and -2°C, compared with thermoconforming torpid bats at mild ambient temperatures (Ta 5-20°C). Tcrit for this species was Ta 0.7 ± 0.4°C, with a corresponding Tb of 1.8 ± 1.2°C. Below Tcrit animals began to thermoregulate, indicated by a considerable but disproportionate increase in both HR and V̇O2. The maximum increase in HR was only 4-fold greater than the average thermoconforming minimum, compared to a 46-fold increase in V̇O2. The differential response of HR and V̇O2 to low Ta was reflected in a 15-fold increase in oxygen delivery per heart beat (cardiac oxygen pulse). During torpor at low Ta, thermoregulating bats maintained a relatively slow HR and compensated for increased metabolic demands by significantly increasing stroke volume and tissue oxygen extraction. Our study provides new information on the relationship between metabolism and HR in an unstudied physiological state that may occur frequently in the wild and can be extremely costly for heterothermic animals.
Data from: Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
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Data from: Measuring embryonic heart rate of wild birds: an opportunity to take the pulse on early development
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Data from: Heart rate reveals torpor at high body temperatures in lowland tropical free-tailed bats
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Data from: Heart rate during hyperphagia differs between two bear species
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Data from: Cold-hearted bats: uncoupling of heart rate and metabolism during torpor at subzero temperatures
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Data from: Body temperature, heart rate, and activity patterns of two boreal homeotherms in winter: homeostasis, allostasis, and ecological coexistence
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Data from: A 45-second self-test for cardiorespiratory fitness: heart rate-based estimation in healthy individuals
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Data from: Flexibility, variability and constraint in energy management strategies across vertebrate taxa revealed by long-term heart rate measurements
1) Animals are expected to be judicious in the use of the energy they gain due to the costs and limits associated with its intake. The management of energy expenditure (EE) exhibited by animals has previously been considered in terms of three patterns: the constrained, independent and performance patterns of energy management. These patterns can be interpreted by regressing daily EE against maintenance EE measured over extended periods. From the multiple studies on this topic, there is equivocal evidence about the existence of universal patterns in certain aspects of energy management. 2) The implicit assumption that animals exhibit specifically one of three discrete energy management patterns, and without variation, seems simplistic. We suggest that animals can exhibit gradations of different energy management patterns and that the exact pattern will fluctuate as their environmental context changes. 3) To investigate these ideas, and for possible large-scale patterns in energy management pattern, we analysed long-term heart rate data – a strong proxy for EE – across and within individuals in 16 species of birds, mammals, and fish. 4) Our analyses of 292 individuals representing 46 539 observation-days suggest that vertebrates typically exhibit predominantly the independent or performance energy patterns at the across-individual level, and that the pattern does not associate with taxonomic group. Within individuals, however, animals generally exhibit some degree of energy constraint. Together, these findings indicate that across diverse species, some individuals supply more energy to all aspects of their life than do others, however all individuals must trade-off deployment of their available energy between competing functions. This demonstrates that within-individual analyses are essential for interpretation of energy management patterns. 5) We also found that species do not necessarily exhibit a fixed energy management pattern but rather temporal variation in their energy management over the year. Animals' energy management exhibited stronger energy constraint during periods of higher EE, which typically coincided with clear and key life cycle events such as reproduction, suggesting an adaptive plasticity to respond to fluctuating energy demands.
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