LongTermBiosignals 2.0.2

IT - Long Term Biosignals Framework

Description

Python library for easy managing and processing of large Long-Term Biosignals. This repository is object of evaluation of some Master's and Doctoral's theses.

Contribute!

• 🪲 Report bugs here.
• 💡 Suggest features here.
• 💬 Community Q&A here.

Informal Documentation

📑 Acess to the Wiki.

Full reference documentarion comming soon.

Getting Started

Installing the Package

This framework was developed and tested on Python 3.10.4, so make sure you have an interperter >= 3.10.4.

If you are familired with pip, you can download and install the package by running:

pip install LongTermBiosignals

If not, you may download the latest stable GitHub release here and place a copy of the ltbio directory (which is inside src) on your project's root.

Dependencies

See Python dependencies in requirements.txt.

You may consider installing the following on your machine if needed:

• graphviz to plot Pipeline diagrams (sudo apt-get install graphviz or brew install graphviz)
• h5py to read HDF5 files if running on an Apple Sillicon machine (brew install hdf5 && export HDF5_DIR=/opt/homebrew/bin/brew/Cellar/hdf5/<version>)

Simple Use Case

Let's create a sequence of samples using Timeseries:

from ltbio.biosignals import Timeseries

ts = Timeseries([1, 2, 3, 4, 5], initial_datetime = datetime.datetime.now(), sampling_frequency = 360.0)

Add let's pretend this was a 1-lead ECG 🫀:

from ltbio.biosignals.modalities import ECG
 
my_ecg = ECG({'Left': ts})

Simple, right? There's loads of more stuff to customize like sources, body locations, physical units, events, etc. Explore it in this notebook 📓 and in the Wiki.

Copyright Notice

There is no license for the content of this repository.

2022, All rights reserved. This means content of this repository cannot be reproduced nor distributed, and no work can be derived from this work without explicit and written permission from the authors.