Why the study?
HRV analysis in unrestrained animals is sensitive to common ECG artefacts, yet most commercial software requires intensive training, extensive manual correction, or is not freely accessible.
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EasieRR is a free, open-source software tool designed to simplify the processing, artifact correction, and analysis of heart rate variability in non-restrained animals.
The assessment of heart rate (HR) and heart rate variability (HRV) based on electrocardiograms (ECG) is considered a good proxy for stress in a wide range of animal species. However, problems can occur e.g., when measuring ECG in ambulatory settings such as recording of unrestrained animals using non‐invasive devices. Artefacts caused by technical (i.e. bad electrode contact) or physiological (i.e. ectopic beats, non‐cardiac muscle potentials) sources are common and can disturb the ECG signal. As HRV analysis is highly sensitive to artefacts in the interbeat interval (RR‐interval) time series the process of visual inspection of the raw signal to detect and correct these is essential. Most of the commercially available software requires intensive training and extensive manual work to accomplish this task and/or is often not available to access for free. EasieRR is an open‐source, stand‐alone software optimized for analysing ECG in non‐restrained animals. The program allows a species‐specific analysis and calculation of recommended standard HRV parameters in both, the time‐ and the nonlinear domain (RMSSD, SDNN, SD1 and SD2). Visualization of data using Poincaré plots and tachograms of RR‐intervals eases the validation of correct heart cycle interval detection and minimizes manual work for the user. Automatically detected peaks can be manually corrected via deletion, correction of spurious detections or marking of undetected peaks. The HRV analysis can be exported using common formats (TXT, MAT). Figures can be plotted and exported in various formats (PDF, SVG, PNG, JPG, TIFF, EMF and EPS). Included in EasieRR is the possibility for synchronization of ECG data with video in order to link cardiac responses to specific behavioural responses.
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Rasmussen et al. (2020) studied this question.
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