Heart Rate Variability (HRV) is essential for evaluating the autonomic nervous system (ANS) function and its interaction with the cardiovascular system (CVS). Its clinical relevance has increased, particularly for detecting autonomic dysfunctions linked to cardiovascular diseases (CVDs), whose prevalence has risen globally. Despite its importance, current HRV software solutions are often costly, closed-source, and lack methodological transparency, limiting their utility in low-resource settings. This project addresses these gaps by developing an open-source, Python-based platform for HRV analysis. It performs signal processing across time, frequency, time-frequency, and nonlinear domains. The software incorporates the pyHRV library, manual R-peak correction, and a Wessel filter to improve tachogram accuracy. Its graphical interface, developed in Tkinter, allows for customizable ECG and tachogram analysis, visualizations, and automated report generation. It was designed to meet the research needs of the Electromechanical Instrumentation Department of the Ignacio Chávez National Institute of Cardiology (INCICh) to understand the autonomic interaction of any type of patient (as an example of a case in this research, records of post-COVID patients were used). Validation against the gold-standard Kubios HRV Premium using 20 ECG recordings showed high agreement across all domains via statistical methods, including Bland-Altman and spectral coherence analyses. This tool offers a reliable, transparent, and cost-effective solution for HRV analysis, supporting clinicians and researchers in improving diagnostics, patient monitoring, and advancing open science.