Ecgmentations is a Python library for ecg augmentation. Ecg augmentation is used in deep learning to increase the quality of trained models. The purpose of ecg augmentation is to create new training samples from the existing data.
Here is an example of how you can apply some augmentations from Ecgmentations to create new ecgs from the original one:
Rostislav Epifanov — Researcher in Novosibirsk
Installation from PyPI:
pip install ecgmentations
Installation from GitHub:
pip install git+https://github.com/rostepifanov/ecgmentations
import numpy as np
import ecgmentations as E
# Declare an augmentation pipeline
transform = E.Sequential([
E.TimeReverse(p=0.5),
E.ChannelShuffle(p=0.06),
])
# Create example ecg (length, nchannels)
ecg = np.ones((12, 5000)).T
# Augment an ecg
transformed = transform(ecg=ecg)
transformed_ecg = transformed['ecg']The list of time axis transforms:
- TimeReverse
- TimeShift
- TimeSegmentShuffle
- RandomTimeWrap
- TimeCutout
- Blur
- Pooling
- TimeCrop
- CenterTimeCrop
- RandomTimeCrop
- TimePadIfNeeded
The list of pulse transforms:
The list of other transforms:
If you find this library useful for your research, please consider citing:
@misc{epifanov2023ecgmentations,
Author = {Rostislav Epifanov},
Title = {Ecgmentations},
Year = {2023},
Publisher = {GitHub},
Journal = {GitHub repository},
Howpublished = {\url{https://github.com/rostepifanov/ecgmentations}}
}