rlign 1.0.post1

Description:

rlign 1.0.post1

Rlign: R peak alignment and ECG transformation framework
This scikit-learn compatible framework rlign is designed to synchronize the temporal variations across ECG recordings. This alignment enables the direct application of simpler machine learning models, like support vector machines and logistic regression, on R-peak aligned ECG signals, bypassing the need for complex and potentially biased feature extraction and allowing for interpretable, efficient analysis with enhanced small sample size convergence. Moreover, the alignment facilitates clustering of ECG time series, overcoming the challenges posed by unaligned data, where clusters are obscured by temporal misalignments of cardiac cycles. Rlign can also be used for improved interpretability of CNNs by aggregating importance maps from Integrated Gradients across all instances of a data set, instead of only reviewing individual ECGs.
Installation
From PyPI
pip install rlign

From source
git clone https://github.com/imi-ms/rlign.git
cd rlign
pip install .

Quick start

Install Rlign
Import Rlign from this package with the corresponding sampling_rate of your data.
Call transform for ECGs with a numpy array of [samples, channels, len].

Examples
You can check out full example notebooks in the example folder.
import rlign

# Create a Normalizer
normalizer = rlign.Rlign(scale_method='hrc')

# call transform with an ecg
# Input shape has to be (samples, channels, len)
ecg_aligned = normalizer.transform(ecg)

# You can update some configurations later on
template_ = rlign.Template(template_bpm=80)
normalizer.update_configuration(template=template_)

ecg_aligned_80hz = normalizer.transform(ecg)

Configurations


sampling_rate: Defines the sampling rate for all ECG recordings.


template: A template ECG created with create_template() method. This template is
used as a reference for aligning R-peaks in the ECG signals.


select_lead: Specifies the lead (e.g., 'Lead II', 'Lead V1') for R-peak detection. Different leads can provide varying levels of
clarity for these features. Selection via channel numbers 0,1,... .


num_workers: Determines the number of CPU cores to be utilized for
parallel processing. Increasing this number can speed up computations
but requires more system resources.


neurokit_method: Chooses the algorithm for R-peak detection from the
NeuroKit package. Different algorithms may offer varying performance
based on the ECG signal characteristics.


correct_artifacts: If set to True, artifact correction is applied
exclusively for R-peak detections, enhancing the accuracy of peak
identification in noisy signals.


scale_method: Selects the scaling method from options like 'resampling'
or 'hrc'. This choice dictates the interval used for resampling
the ECG signal, which can impact the quality of the processed signal.


remove_fails: Determines the behavior when scaling is not possible. If
set to True, the problematic ECG is excluded from the dataset. If False,
the original, unscaled ECG signal is returned instead.


median_beat: Calculates the median from a set of aligned beats
and returns a single, representative beat.


silent: Disable all warnings.


Citation
Please use the following citation:
License
MIT License