ecgclock

ECG clock plotter


Keywords
Holter, ECG, EKG, clock, QT, LQTS
License
MIT
Install
pip install ecgclock==2019.7.26

Documentation

ECG Clock Plotter

This repository contains a python library for simple plotting of ECG data (typically QTc values) in the "24 hour clock" format:

Example QT Clock

DISCLAIMER:

All of the included default values, thresholds/ranges, example data sets, etc. are for illustration, not for diagnostic use. It is the clinician's responsibility to adjust all settings appropriately.

How do I get set up?

You will need Python 3 with the following modules available:

Depending on the chosen matplotlib backend, there may be other dependencies, such as PySide/PySide2 or PyGTK.

To install from PyPI:

pip3 install ecgclock

Or from git:

git clone https://bitbucket.org/atpage/ecgclock.git
cd ecgclock
pip3 install -e .

How do I run it?

ECGClock is the main class that allows you to create and plot an ECG Clock. It is subclassed as QTClock, etc.

There are two main things (classes) that you would want to add to a clock: the Recording, and the Cohort. A Recording is a set of measurements from a single ECG recording (e.g. a 24-hour Holter). It will be plotted as a continous line on the clock (see blue and purple lines in the example). A Cohort is a set of Recordings, from which you want to plot the typical range of some measured value. For example, you may want to plot the IQR of QTc in a Cohort of healthy adults, to see how it compares to another Cohort or individual Recording. The green region in the example was created by plotting one Cohort with two different percentile ranges.

The basic process to make a clock, then, is:

  1. Create an ECGClock.
  2. Add 1 or more Recordings and/or Cohorts to the clock.
  3. Configure additional properties of the clock, such as the legend, or highlighting dangerous value ranges.
  4. Save the clock as an image file, or show it in an interactive window.

Step 2 may also require some pre-processing, e.g. computing heart rate corrected columns that weren't provided in the original measurements.

For example code, see test_simple_clock() and test_complex_clock() in test_ecgclock.py.

In Windows you should run multiprocessing.freeze_support() at the beginning of any __main__ function.

This package also includes a make_qtclock command. Run make_qtclock -h for details.

Note on API changes:

Usage of this library was originally documented in this article. However, significant refactoring of the code is taking place in 2018, including API changes. The article is still a good reference, but specific code examples will no longer work as-is.

To use the old version of the library, roll back to commit 7e4d4ce (which is tagged as 'old').

Who do I talk to?