JoVE Logo
Faculty Resource Center

Sign In





Representative Results






Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published: May 23rd, 2021



1Department of Medicine I, University Hospital Munich, Campus Großhadern, Ludwig-Maximilians University Munich (LMU), 2DZHK (German Centre for Cardiovascular Research), Partner Site Munich, Munich Heart Alliance (MHA), 3Walter Brendel Centre of Experimental Medicine, Ludwig-Maximilians University Munich (LMU)

Here we present a step-by-step protocol for a semiautomated approach to analyze murine long-term electrocardiography (ECG) data for basic ECG parameters and common arrhythmias. Data are obtained by implantable telemetry transmitters in living and awake mice and analyzed using Ponemah and its analysis modules.

Arrhythmias are common, affecting millions of patients worldwide. Current treatment strategies are associated with significant side effects and remain ineffective in many patients. To improve patient care, novel and innovative therapeutic concepts causally targeting arrhythmia mechanisms are needed. To study the complex pathophysiology of arrhythmias, suitable animal models are necessary, and mice have been proven to be ideal model species to evaluate the genetic impact on arrhythmias, to investigate fundamental molecular and cellular mechanisms, and to identify potential therapeutic targets.

Implantable telemetry devices are among the most powerful tools available to study electrophysiology in mice, allowing continuous ECG recording over a period of several months in freely moving, awake mice. However, due to the huge number of data points (>1 million QRS complexes per day), analysis of telemetry data remains challenging. This article describes a step-by-step approach to analyze ECGs and to detect arrhythmias in long-term telemetry recordings using the software, Ponemah, with its analysis modules, ECG Pro and Data Insights, developed by Data Sciences International (DSI). To analyze basic ECG parameters, such as heart rate, P wave duration, PR interval, QRS interval, or QT duration, an automated attribute analysis was performed using Ponemah to identify P, Q, and T waves within individually adjusted windows around detected R waves.

Results were then manually reviewed, allowing adjustment of individual annotations. The output from the attribute-based analysis and the pattern recognition analysis was then used by the Data Insights module to detect arrhythmias. This module allows an automatic screening for individually defined arrhythmias within the recording, followed by a manual review of suspected arrhythmia episodes. The article briefly discusses challenges in recording and detecting ECG signals, suggests strategies to improve data quality, and provides representative recordings of arrhythmias detected in mice using the approach described above.

Cardiac arrhythmias are common, affecting millions of patients worldwide1. Ageing populations show a growing incidence and thus a major public health burden resulting from cardiac arrhythmias and their morbidity and mortality2. Current treatment strategies are limited and often associated with significant side effects and remain ineffective in many patients3,4,5,6. Novel and innovative therapeutic strategies that causally target arrhythmia mechanisms are urgently needed. To study the complex pa....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

1. Prearrangements

  1. Start Ponemah 6.42 software, and confirm the username and serial number of the software license on the following screen by clicking on Continue.
  2. Load the experiment containing the ECG of interest
    1. If Ponemah is started for the first time, note that the Ponemah Get Started dialog opens, offering three options: 1) Create Experiment, 2) Load Experiment, 3) Import Experiment.
      1. Select Load Experiment to open a f.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

Recording long-term ECGs results in huge data sets. The options for further analyses are manifold and depend on the individual research project. This protocol provides a description of some very basic readouts that can be used by most researchers, especially for screening experiments, e.g., when characterizing a transgenic mouse line or when investigating the effects of a specific treatment in a disease model. A previous project involved the study of a novel drug candidate to determine whether it possessed cardi.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

The surface ECG is the primary diagnostic tool for patients suffering from heart rhythm disorders, providing insights into many electrophysiological phenomena. Nevertheless, sufficient analysis of cardiac surface ECG pathologies requires knowledge and definition of normal physiologic parameters. Many years of epidemiological research have led to broad consent on what is physiologic in humans and thus enabled physicians worldwide to clearly distinguish the pathologic. However, the analysis of surface ECG data is a major c.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

This work was supported by German Research Foundation (DFG; Clinician Scientist Program In Vascular Medicine (PRIME), MA 2186/14-1 to P. Tomsits and D. Schüttler), German Centre for Cardiovascular Research (DZHK; 81X2600255 to S. Clauss), the Corona Foundation (S199/10079/2019 to S. Clauss), the ERA-NET on Cardiovascular Diseases (ERA-CVD; 01KL1910 to S. Clauss), the Heinrich-and-Lotte-Mühlfenzl Stiftung (to S. Clauss) and the China Scholarship Council (CSC, to R. Xia). The funders had no role in manuscript preparation.


Log in or to access full content. Learn more about your institution’s access to JoVE content here

Name Company Catalog Number Comments
Ponemah Software Data Science international ECG Analysis Software

  1. Camm, A. J., et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Europace. 12 (10), 1360-1420 (2010).
  2. Chugh, S. S., et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation. 129 (8), 837-847 (2014).
  3. Dobrev, D., et al. New antiarrhythmic drugs for treatment of atrial fibrillation. Lancet. 375 (9721), 1212-1223 (2010).
  4. January, C. T., et al. 2019 AHA/ACC/HRS focused update of the 2014 AHA/ACC/HRS Guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons. Circulation. 140 (2), 125-151 (2019).
  5. Heijman, J., et al. Cardiac safety assays. Current Opinion in Pharmacology. 15, 16-21 (2014).
  6. Kirchhof, P., et al. ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. European Heart Journal. 37 (38), 2893-2962 (2016).
  7. Clauss, S., et al. Animal models of arrhythmia: classic electrophysiology to genetically modified large animals. Nature reviews. Cardiology. 16 (8), 457-475 (2019).
  8. Schüttler, D., et al. Animal models of atrial fibrillation. Circulation Research. 127 (1), 91-110 (2020).
  9. Dobrev, D., et al. Mouse models of cardiac arrhythmias. Circulation Research. 123 (3), 332-334 (2018).
  10. Rosero, S. Z., et al. Ambulatory ECG monitoring in atrial fibrillation management. Progress in cardiovascular diseases. 56 (2), 143-152 (2013).
  11. Russell, D. M., et al. A high bandwidth fully implantable mouse telemetry system for chronic ECG measurement. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology. 2011, 7666-7669 (2011).
  12. McCauley, M. D., et al. Ambulatory ECG recording in mice. Journal of Visualized Experiments : JoVE. (39), e1739 (2010).
  13. Mehendale, A. C., et al. Unlock the information in your data: Software to find, classify, and report on data patterns and arrhythmias. Journal of Pharmacological and Toxicological Methods. 81, 99-106 (2016).
  14. Hulsmans, M., et al. Macrophages facilitate electrical conduction in the heart. Cell. 169 (3), 510-522 (2017).
  15. Boukens, B. J., et al. Misinterpretation of the mouse ECG: 'musing the waves of Mus musculus. Journal of Physiology. 592 (21), 4613-4626 (2014).
  16. Wehrens, X. H., et al. Mouse electrocardiography: an interval of thirty years. Cardiovascular Research. 45 (1), 231-237 (2000).
  17. Goldbarg, A. N., et al. Electrocardiogram of the normal mouse, Mus musculus: general considerations and genetic aspects. Cardiovascular Research. 2 (1), 93-99 (1968).
  18. Kaese, S., et al. The ECG in cardiovascular-relevant animal models of electrophysiology. Herzschrittmachertherapie und Elektrophysiologie. 24 (2), 84-91 (2013).
  19. Speerschneider, T., et al. Physiology and analysis of the electrocardiographic T wave in mice. Acta Physiologica. 209 (4), 262-271 (2013).
  20. Toib, A., et al. Remodeling of repolarization and arrhythmia susceptibility in a myosin-binding protein C knockout mouse model. American Journal of Physiology. Heart and Circulatory Physiology. 313 (3), 620-630 (2017).
  21. Thireau, J., et al. Heart rate variability in mice: a theoretical and practical guide. Experimental Physiology. 93 (1), 83-94 (2008).
  22. Hilgard, J., et al. Significance of ventricular pauses of three seconds or more detected on twenty-four-hour Holter recordings. American Journal of Cardiology. 55 (8), 1005-1008 (1985).

This article has been published

Video Coming Soon

JoVE Logo


Terms of Use





Copyright © 2024 MyJoVE Corporation. All rights reserved