A Computational Tool for Evaluation of Heart Rate Variability in Rats

Mariana da Palma Valério1, Silvia Becker2, Higor Barreto Campos3, Pietra Aguiar dos Reis4, Vlasta Bari5, Luan de Almeida Moura4, Axel Loewe6, Alessandro Pereira da Silva4
1University of Mogi das Cruzes, 2Karlsruhe Institute of Technology, Universitäts-Herzzentrum Freiburg - Bad Krozingen, 3Universidade Mogi das Cruzes, 4Universidade de Mogi das Cruzes, 5Department of Biomedical Sciences for Health, University of Milan, 6Karlsruhe Institute of Technology (KIT)

Abstract

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.