Outliers and artefacts detection

This example shows how to detect ectopic, missed, extra, slow and long long from RR or pulse rate interval time series using the method proposed by Lipponen & Tarvainen (2019) 1.

# Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>
# Licence: GPL v3

from systole.detection import rr_artefacts
from systole.plots import plot_subspaces
from systole.utils import simulate_rr

RR artefacts

The proposed method will detect 4 kinds of artefacts in an RR time series: Missed R peaks, when an existing R component was erroneously NOT detected by the algorithm. * Extra R peaks, when an R peak was detected but does not exist in the signal. * Long or short interval intervals, when R peaks are correctly detected but the resulting interval has extreme value in the overall time-series. * Ectopic beats, due to disturbance of the cardiac rhythm when the heart either skip or add an extra beat. * The category in which the artefact belongs will have an influence on the correction procedure (see Artefact correction).

Simulate RR time series

This function will simulate RR time series containing ectopic, extra, missed, long and short artefacts.

rr = simulate_rr()

Artefact detection

outliers = rr_artefacts(rr)

Subspaces visualization

You can visualize the two main subspaces and spot outliers. The left pamel plot subspaces that are more sensitive to ectopic beats detection. The right panel plot subspaces that will be more sensitive to long or short beats, comprizing the extra and missed beats.

plot_subspaces(rr, figsize=(12, 6))

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(<Axes: title={'center': 'Subspace 1 \n (ectopic beats detection)'}, xlabel='Subspace $S_{11}$', ylabel='Subspace $S_{12}$'>, <Axes: title={'center': 'Subspace 2 \n (long and short beats detection)'}, xlabel='Subspace $S_{21}$', ylabel='Subspace $S_{22}$'>)

References

1

Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for heart rate variability time series artefact correction using novel beat classification. Journal of Medical Engineering & Technology, 43(3), 173–181. https://doi.org/10.1080/03091902.2019.1640306