rlign 1.0.post1

Beat normalization for ECG data.

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

1. Install Rlign
2. Import Rlign from this package with the corresponding sampling_rate of your data.
3. 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