systole.plots.plot_ectopic

systole.plots.plot_ectopic(rr: None, artefacts: Dict[str, ndarray], input_type: str = 'rr_ms') → figure | Axes

systole.plots.plot_ectopic(rr: List[float] | ndarray, artefacts: None, input_type: str = 'rr_ms') → figure | Axes

systole.plots.plot_ectopic(rr: List[float] | ndarray, artefacts: Dict[str, ndarray], input_type: str = 'rr_ms') → figure | Axes

Visualization of ectopic beats detection.

The artefact detection is based on the method described in [1].

Parameters:

rr

Interval time-series (R-R, beat-to-beat…), in miliseconds.

artefacts

The artefacts detected using systole.detection.rr_artefacts().

input_type

The type of input vector. Default is “rr_ms” for vectors of RR intervals, or interbeat intervals (IBI), expressed in milliseconds. Can also be “peaks” (a boolean vector where 1 represents the occurrence of R waves or systolic peaks) or “rr_s” for IBI expressed in seconds.

ax

Where to draw the plot. Default is None (create a new figure). Only applies when backend=”matplotlib”.

backend

Select plotting backend (“matplotlib” or “bokeh”. Defaults to “matplotlib”.

figsize

Figure size. Default is (13, 5) for Matplotlib backend, and the height is 600 when using Bokeh backend.

Returns:

plot

The matplotlib axes, or the boken figure containing the plot.

See also

plot_shortlong, plot_subspaces

Notes

If both rr and artefacts are provided, the function will drop artefacts and re-evaluate given the current RR time-series.

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

Examples

Visualizing ectopic subspace from RR time series.

from systole import import_rr
from systole.plots import plot_ectopic

# Import PPG recording as numpy array
rr = import_rr().rr.to_numpy()

plot_ectopic(rr, input_type="rr_ms")

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

Visualizing ectopic subspace from the artefact dictionary generated by systole.detection.rr_artefacts().

from systole.detection import rr_artefacts

# Use the rr_artefacts function to find ectopic beats
artefacts = rr_artefacts(rr)

plot_ectopic(artefacts=artefacts)

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

Using Bokeh as plotting backend.

from bokeh.io import output_notebook
from bokeh.plotting import show
from systole.detection import rr_artefacts
output_notebook()

# Use the rr_artefacts function to find ectopic beats
artefacts = rr_artefacts(rr)
show(
    plot_ectopic(artefacts=artefacts, backend="bokeh")
 )

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