Measuring heart rate variability (HRV) can provide a deeper understanding of many disease states including cardiovascular, stroke, diabetes, alcoholism, cancer, glaucoma, and more. HRV is the physiologic phenomenon of variation in the time interval between heartbeats and can be observed as the heartbeat changes when a body is at rest and during exercise. High HRV is an indication of healthy autonomic and cardiovascular response. Low HRV may indicate that the sympathetic and parasympathetic nervous systems are not properly coordinating to provide an appropriate heart rate response. Factors such as disease, medications, and age can affect the heart’s ability to produce this variability.
HRV Analysis requires a series of successive heartbeat intervals and is typically derived from the R-R intervals of ECG signals or inter-beat-intervals from blood pressure signals. Analysis methods for HRV data exist in the time-domain and frequency-domain. Each method of analysis is very different but contains a wealth of information.
If you are interested in learning more about HRV analysis, additional details are available here.
HRV Publications Citing Use of DSI Technology
Impact of repeated kindled seizures on heart rate rhythms, heart rate variability, and locomotor activity in rats
This study aimed to better understand the effect of seizures on activity levels and heart rate, as it may contribute to disease burden. The research team used a kindling model, a common, reliable chronic seizure model which uses an implanted depth electrode to create stimulation. Prior to inducing seizures, the rats were implanted with DSI’s ETA-F10 implant to measure ECG. Using Ponemah software, they were able to analyze the data to calculate HRV. The results showed a significant impact of seizures on heart rate and activity which may have implications for treatment strategies.1
Acclimation to a thermoneutral environment abolishes age-associated alterations in heart rate and heart rate variability in conscious, unrestrained mice
Although mice are a translational model for cardiovascular aging, low laboratory room temperatures introduce a cold stress variable to studies that humans do not experience. This study aimed to understand the impact of cold stress on autonomic modulation of heart rate and heart rate variability to improve translation. The team used DSI’s telemetry solution to measure ECG throughout the study. The results of this study indicate that to ensure translation of mouse studies to humans, mice must be adequately acclimated to their metabolic thermoneutral environment.2
Resting Heart Rate Variability Predicts Vulnerability to Pharmacologically-Induced Ventricular Arrhythmias in Male Rats
Reduced HRV has been proposed as a predictor of sudden death from ventricular arrhythmia in cardiac patients, but risk in patients without pre-existing conditions has not been adequately investigated. This research team intended to examine the link between resting state HRV and susceptibility to spontaneous and pharmacologically induced ventricular arrhythmias in healthy rats. They used DSI’s solutions to collect ECG data during resting periods and pharmacological stimulation and analyzed the data to measure HRV. The study’s results did not show a significant correlation between individual differences in resting HRV and spontaneous arrhythmias. However, they did find that lower resting HRV was associated with a higher number of ventricular ectopic beats following pharmacological stimulation.3
The Use of Percent Change in RR Interval for Data Exclusion in Analyzing 24-h Time Domain Heart Rate Variability in Rodents
Although we know HRV measurements can assist in understanding several diseases, the mechanisms behind changes in HRV and disease progression have not been sufficiently evaluated. This is, in part, due to the complexity of analyzing large datasets. This research team evaluated an R-R interval exclusion method based on percent (%) change of adjacent R-R intervals in mouse and rat models. Two approaches were evaluated: % change from “either” and “both” adjacent R-R intervals. The data exclusion method based on standard deviation (SD) was also evaluated for comparison. They used DSI solutions in both models to measure blood pressure and ECG. Data Insights, an analysis module in DSI’s Ponemah software, was used to automate a search for abnormal R-R intervals so they could be removed from analysis. The team found that a 20% change from “either” adjacent R-R intervals is a good criterion for data exclusion for reliable 24-h time domain HRV analysis in rodents.4
DSI Solutions for measuring HRV
Data Collection Methods
DSI’s flexible system provides multiple options for collecting the ECG and blood pressure data that are key to HRV analysis. Implantable telemetry enables you to collect high-quality continuous data from conscious, freely moving animals of multiple species including mice, rats, dogs, and non-human primates. Jacketed External Telemetry is a minimally invasive method for collecting ECG and blood pressure data, ideal in acute or high-throughput studies. Hardwired instrumentation can also be used in acute studies to collect ECG data through use of a tethered system as well as blood pressure while under chemical restraint.
Data Acquisition and Analysis Software
Ponemah is a complete physiologic data acquisition and analysis software platform used by physiologists, pharmacologists, and toxicologists to confidently collect, accurately analyze, and quickly summarize study data. Its Data Insights module is of interest for HRV analysis and arrhythmia detection as it allows you to execute automated, customizable searches to expose patterns and anomalies in your data. Learn more about Data Insights here.
Want a more in-depth look at the power Data Insights could bring to your research or additional information on HRV analysis solutions? Schedule a free consultation with us today!
- Möller C, Maarten van Dijk R, Wolf F, Keck M, Schönhoff K, Bierling V, Potschka H. (2019). “Impact of repeated kindled seizures on heart rate rhythms, heart rate variability, and locomotor activity in rats.” Epilepsy & Behavior. 92, 36-44. https://doi.org/10.1016/j.yebeh.2018.11.034.
- Axsom JE, Nanavati AP, Rutishauser CA, Bonin JE, Moen JM, Lakatta EG. (2019). “Acclimation to a thermoneutral environment abolishes age-associated alterations in heart rate and heart rate variability in conscious, unrestrained mice”. GeroScience. 42, 217-232. https://doi.org/10.1007/s11357-019-00126-7.
- Carnevali L, Statello R, Sgoifo A. (2019). “Resting Heart Rate Variability Predicts Vulnerability to Pharmacologically-Induced Ventricular Arrhythmias in Male Rats”. Journal of Clinical Medicine. 8(5), 655. https://doi.org/10.3390/jcm8050655.
- Karey E, Pan S, Morris AN, Bruun DA, Lein PJ, Chen CY. (2019). “The Use of Percent Change in RR Interval for Data Exclusion in Analyzing 24-h Time Domain Heart Rate Variability in Rodents”. Frontiers in Physiology. 10, 693. https://doi.org/10.3389/fphys.2019.00693.