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Updated: Oct 26, 2025
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
Published on: May 23, 2021
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Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the
Nicolle J Domnik1,2, Sami Torbey3, Geoffrey E J Seaborn3
1Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada. n.j.domnik@queensu.ca.
Summary
A new algorithm called MAST offers automated, accurate R-wave detection from electrocardiogram (ECG) data in mice. This noise-robust method improves heart rate analysis and reduces errors compared to commercial software.
Area of Science:
- Physiology
- Biomedical Engineering
- Computational Biology
Background:
- Biologging devices provide high-resolution ambulatory electrocardiogram (ECG) data for in vivo cardiopulmonary research.
- Automated analysis and flexible quality-control are crucial for handling large datasets from these devices.
- Existing automated software can underestimate heart rate in the presence of high-amplitude noise.
Purpose of the Study:
- To develop a novel, open-access algorithm for automated, accurate, and noise-robust R-wave detection from single-channel ECG recordings in mice.
- To provide a flexible quality-control method that handles signal artefacts effectively.
- To create a foundational code adaptable for use in various species.
Main Methods:
- Development of the Moving Average and Standard Deviation Thresholding (MAST) algorithm for R-wave detection.
- Automated exclusion and annotation of ECG segments with excessive artefact levels.
- Blind comparison of MAST's performance against a commercial ECG analysis program using 270 mouse ECG recordings with varying artefacts.
Main Results:
- MAST demonstrated a significantly lower error rate (approximately one quarter) compared to the commercial software.
- MAST achieved higher accuracy and consistency in R-wave detection, with virtually no false positives.
- Statistical analysis showed significant improvements in sensitivity (98.48% ± 4.32% vs. 94.59% ± 17.52%) and positive predictivity (99.99% ± 0.06% vs. 99.57% ± 3.91%) for MAST (P < 0.001 and P = 0.0274, respectively).
Conclusions:
- The novel, open-access MAST algorithm enables more accurate and less effortful analysis of murine heart rate indices.
- MAST's automated artefact handling and customizable settings enhance its utility for physiological research.
- The algorithm serves as a foundational code for potential translation to other species, including mammals, birds, and ectothermic vertebrates.
