pyheartlib.data_arrhythmia

Module Contents

Classes

ArrhythmiaData

Processes ECG records to make a dataset holding records along with

Attributes

msg

msg

pyheartlib.data_arrhythmia.msg = 'Module data_arrhythmia will be deprecated in future versions. Use module data_rhythm instead.'
pyheartlib.data_arrhythmia.msg = 'ArrhythmiaData class will be deprecated. Use RhythmData class instead.'
class pyheartlib.data_arrhythmia.ArrhythmiaData(*args, **kwargs)

Bases: pyheartlib.data_rhythm.RhythmData

Processes ECG records to make a dataset holding records along with metadata about signal excerpts.

It has a method that can generate metadata for signal excerpts. The metadata are generated using the sliding window approach. For each excerpt of a signal the onset, offset, and its annotation is recorded. The metadata list for an excerpt is structured as: [record_id, onset, offset, annotation]. Annotation for an excerpt is a single label. Example metadata for an excerpt: [10, 500, 800, ‘AFIB’].

Parameters:

  • base_path (str, optional) – Path of the main directory for storing the original and processed data, by default None

  • remove_bl (bool, optional) – If True, the baseline wander is removed from the original signals prior to extracting excerpts, by default False

  • lowpass (bool, optional) – Whether or not to apply low-pass filter to the original signals, by default False

  • cutoff (int, optional) – Parameter of the low pass-filter, by default 45

  • order (int, optional) – Parameter of the low pass-filter, by default 15

  • progress_bar (bool, optional) – Whether to display a progress bar, by default True

  • processors (list, optional) – Ordered list of functions’ names for preprocessing the raw signals. Each function takes a one-dimensional NumPy array as its input and returns an array of the same length.

Example

>>> from pyheartlib.data_rhythm import RhythmData
>>> # Make an instance of the RhythmData
>>> rhythm_data = RhythmData(
>>>     base_path="data", remove_bl=False, lowpass=False,
>>>     progress_bar=False)
>>> # Define records
>>> train_set = [201, 203]
>>> # Create the dataset
>>> rhythm_data.save_dataset(
>>>   rec_list=train_set, file_name="train.arr", win_size=3600, stride=64
>>> )