A Computational Tool for Evaluation of Heart Rate Variability in Rats

This study presents ECGRats, a software with a graphical user interface (GUI) that calculates heart rate variability features in rats, such as SDNN, rMSSD, NN20, and pNN20. The algorithm was developed in Python using high-pass, low-pass, and notch filters, along with a Haar wavelet transform to identify R-peaks. ECG data from Wistar rats, provided by the University of Parma, Italy, were used. 100 ECG recordings were grouped into sets of ten per rat. Each signal underwent baseline correction to remove DC offset before HRV parameters were extracted. HRV analysis included both time-domain and frequency- domain features, providing insights into autonomic nervous system activity and cardiac arrhythmia. Data dispersion was significant in some groups with variations from 300 to 460 bpm, whereas other groups had lower variability, indicating a more homogeneous distribution. rMSSD was generally higher than SDNN, indicating greater short-term variability. Additionally, NN20 values were higher than pNN20, as expected. The tool's accuracy was validated by comparison with manual heart rate variability analysis methods, showing correlation. The results suggest that the tool may be useful for assessing heart rate variability in animal studies.