Abstract
Hrv is an open-source Data Science project. A Python package for heart rate variability analysis. techniques to extract information about cardiac autonomic functions through RRi series and Heart Rate Variability (HRV) analyses without losing the Power and Flexibility of a native Python object. It is built using Jupyter Notebook, Python. The complete source code is publicly available on GitHub under the BSD 3-Clause "New" or "Revised" License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
techniques to extract information about cardiac autonomic functions through RRi series and Heart Rate Variability (HRV) analyses without losing the Power and Flexibility of a native Python object.
In other words, the hrv module eases the manipulation, inspection, pre-processing, visualization, and analyses of HRV-related information. Additionally, it is written with idiomatic code and tries to implement the API of a built-in object, which might make it intuitive for Python users.
For a more in-depth explanation of how hrv works, please Read the documentation
2. Objective
A Python package for heart rate variability analysis
This project demonstrates how Jupyter Notebook, Python can be applied to a real-world Data Science problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/rhenanbartels/hrv.git
cd hrvFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Turn the analysis into an interactive dashboard
- Automate data refresh with a scheduled job
- Add a predictive model on top of the analysis
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by rhenanbartels and published on GitHub under the BSD 3-Clause "New" or "Revised" License. Please follow the license terms and credit the original author when you use or modify this code.
Want to build this as your internship project?
Work on a Data Science project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.
Apply for Data Science Internship