ECG heartbeat classification
============================
This document describes how to classify heartbeats according to its
origin.
Description
-----------
This task implements a heartbeat classifier that follows the `EC-57 AAMI
recommendation <http://marketplace.aami.org/eseries/scriptcontent/docs/Preview%20Files/EC57_1212_preview.pdf>`__
classifying heartbeats into four classes:
- **N** normal
- **S** supraventricular
- **V** ventricular
- **F** fusion of normal and ventricular
Certain background and introduction to this topic is included in
my `PhD thesis <http://i3a.unizar.es/postgrado/descarga_tesis_pdf.php?ver=48>`__.
Input Arguments
---------------
``progress_handle`` — Used to track the progress within your function. ``[] (default)``
progress\_handle, is a handle to a :doc:`progress\_bar <progress_bar>`
object, that can be used to track the progress within your function.
``tmp_path`` — The path to store temporary data. ``tempdir() (default)``
Full path to a directory with write privileges.
```payload`` — A structure to provide audited heartbeat detections to the classifier algorithm. ``[] (default)``
This variable is useful to pass automatic or corrected QRS detections to the classification task.
This can be performed as shown in the following example:
.. code::
cached_filenames = ECGw.GetCahchedFileName({'QRS_corrector' 'QRS_detection'});
ECGw.ECGtaskHandle.payload = load(cached_filenames{1});
``mode`` — Set the classification mode of operation. ``'auto' (default)``
A control string with any of the following names
- 'auto', this mode makes the algorithm operate in automatic mode.
- 'slightly-assisted', this mode requires that an expert labels several
representative examples, when the algorithm does not reach a confidence
level to do it automatically.
- 'assisted', this mode is completely assisted. An expert must label all
the representative heartbeats from each cluster.
Examples
--------
The first example shows the simplest setup of the
*ECGtask\_heartbeat\_classifier* object, while at the end of this section
a complete example with a real signal is shown.
.. code::
% with the task name
ECG_w.ECGtaskHandle = 'ECG_heartbeat_classifier';
% or create an specific handle to have more control
ECGt = ECGtask_heartbeat_classifier();
and then you are ready to setup the task
.. code::
% select a mode, automatic mode does not require assistance
ECGt.mode = 'auto';
% this is to use QRS detection previously calculated
cached_filenames = ECG_all_wrappers(ii).GetCahchedFileName({'QRS_corrector' 'QRS_detection'});
ECGt.payload = load(cached_filenames{1})
Finally set the task to the wrapper object, and execute the task.
.. code::
ECG_w.ECGtaskHandle= ECGt; % set the ECG task
ECG_w.Run();
This example shows in first place, the previous configuration used in
recording 208 from MIT Arrhythmia database.
.. code::
>> ECG_w = ECGwrapper( ...
'recording_name', 'some_path\208', ...
'recording_format', 'MIT', ...
'ECGtaskHandle', 'ECG_heartbeat_classifier', ...
)ECG_w =
############################
# ECGwrapper object config #
############################
+ECG recording: some_path\208 (auto)
+PID: 1/1
+Repetitions: 1
+Partition mode: ECG_overlapped
+Function name: ECG_heartbeat_classifier
+Processed: false
>> ECG_w.Run();
You can follow the evolution in the progress bar, and after a while, it
ends and display the classification results
.. code-block:: none
Configuration
-------------
+ Recording: ... \example recordings\208.dat (MIT)
+ Mode: auto (12 clusters, 1 iterations, 75% cluster-presence)
True | Estimated Labels
Labels | Normal Suprav Ventri Unknow| Totals
-----------------|----------------------------|-------
Normal | 1567 6 13 0 | 1586
Supraventricular| 2 0 0 0 | 2
Ventricular | 255 8 1102 0 | 1365
Unknown | 2 0 0 0 | 2
-----------------|----------------------------|-------
Totals | 1826 14 1115 0 | 2955
Balanced Results for
---------------------
| Normal || Supravent || Ventricul || TOTALS |
| Se +P || Se +P || Se +P || Acc | Se | +P |
| 99% 45% || 0% 0% || 81% 99% || 60% | 60% | 48% |
Unbalanced Results for
-----------------------
| Normal || Supravent || Ventricul || TOTALS |
| Se +P || Se +P || Se +P || Acc | Se | +P |
| 99% 86% || 0% 0% || 81% 99% || 90% | 60% | 62% |
This is possible because this recording include the expert annotations,
or ''ground truth'', for each heartbeat. The manual annotations in MIT
format are typically included in ''.atr'' files (in this case
''208.atr''). Now you can try ''slightly-assisted'' mode, where the
algorithm may ask you for help in case of cluster heterogeneity. If this
happens, a window like this will appear:
|image4|
In this window the algorithm is asking you to label the centroid of the
cluster, that is showed in the left panel. In the top of each panel some
information is showed, as the amount of heartbeats in the current
cluster. In the middle panel, you have some examples of heartbeats close
to the centroid in a likelihood sense. The same is repeated in the right
panel, but with examples far from the centroid. This manner you can have
an idea of the dispersion of heartbeats within a cluster. Large
differences across the panels indicates large cluster dispersion. If you
decide to label the cluster, you can use one of the 4 buttons on your
right. The unknown class is reserved for the cases where you can not
make a confident decision. At the same time, in the command window, a
suggestion appears:
.. code-block:: none
Configuration
-------------
+ Recording: .\example recordings\208.dat (MIT)
+ Mode: assisted (3 clusters, 1 iterations, 75% cluster-presence)
Suggestion: Normal
This means that the centroid heartbeat in the ''.atr'' file is labeled
as ''Normal''. You will see this suggestion for each cluster analyzed,
if there are annotations previously available. You are informed about
the percentage of heartbeats already labeled with a progress bar, in the
bottom of the control panel window.
In case you believe that a cluster includes several classes of
heartbeats, you can decide to ''skip'' the classification, and try to
re-cluster those heartbeats in the next iteration. You are free to
perform as many iterations as you decide, by skipping clusters. The
refresh button resamples heartbeats close and far from the centroid, and
then redraw the middle and right panels. This feature is useful for
large clusters.
You can check the result of this task for every heartbeat in the
recording using the :doc:`visualization functions <plot_ecg_strip>`.
Also check this
:ref:`example <Automatic_Heartbeat_classification>` for
further information.
.. _Classifier_det_result_format:
Results format
--------------
The result file will have two variables, the annotation type or classification label ``anntyp``,
containing a ``char`` label per heartbeat. And a vector of samples called ``time`` (in correspondence with
``anntyp``), with the occurrence of all heartbeats used in this task.
More About
----------
Here are some external references about heartbeat classification:
- `EC-57 AAMI
recommendation <http://marketplace.aami.org/eseries/scriptcontent/docs/Preview%20Files/EC57_1212_preview.pdf>`__
- `EP limited <http://www.eplimited.com/confirmation.htm>`__ software
See Also
--------
:doc:`ECGtask <ECGtask>` \| :doc:`QRS detection <QRS_detection>` \| :doc:`examples <examples>`
.. |image4| image:: 2D__Mariano_misc_a2hbc_doc_expert_user_interface.png