Metadata-Version: 2.1
Name: pobm
Version: 1.2.0
Summary: Oximetry Toolbox, for extracting biomarkers from spo2 signal
Home-page: http://aim-lab.github.io
Author: "Jeremy Levy"
Author-email: "jeremy.levy@campus.technion.ac.il"
License: proprietary
Project-URL: Documentation, http://aim-lab.github.io
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: testing
License-File: LICENSE.txt
License-File: AUTHORS.rst

# PhysioZoo OBM documentation

Oximetry digital biomarkers for the analysis of continuous oximetry (SpO2) time series.

Based on the paper 
Jeremy Levy, Daniel ́Alvarez, Aviv A Rosenberg, Alexandra Alexandrovich, F ́elix Del Campo, and Joachim ABehar.  Digital oximetry biomarkers for assessing respiratory function:  standards of measurement, physiologicalinterpretation, and clinical use.NPJ digital medicine, 4(1):1–14, 2021

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## Description

Five types of biomarkers may be evaluated:

1.  General statistics: time-based statistics describing the oxygen saturation time series data distribution.

2.  Complexity: quantify the presence of long-range correlations in non-stationary time series.

3.  Periodicity: quantify consecutive events creating some periodicity in the oxygen saturation time series.

4.  Desaturations: time-based measures that are descriptive statistics of the desaturation patterns happening throughout the time series.

5.  Hypoxic burden: time-based measures quantifying the overall degree of hypoxemia imposed to the heart and other organs during the recording period.

## Installation

Available on pip, with the command: 
pip install pobm

pip project: https://pypi.org/project/pobm/

## Requirements

numpy > 1.18.2

scikit-learn > 0.22.2

scipy > 1.4.1

lempel-ziv-complexity==0.2.2

All the requirements are installed when the toolbox is installed, no need for additional commands.

## Documentation

Available at https://oximetry-toolbox.readthedocs.io/en/latest/
