def test_plot_hr(self):
ax = plot_hr(oxi)
assert isinstance(ax, matplotlib.axes.Axes)
ax = plot_hr(oxi.peaks)
assert isinstance(ax, matplotlib.axes.Axes)
outliers = np.where(oxi.peaks)[0][:2]
ax = plot_hr(np.asarray(oxi.peaks), unit='bpm', outliers=outliers)
assert isinstance(ax, matplotlib.axes.Axes)
def plot_hr(self, ax=None):
"""Plot heartrate extracted from PPG recording.
Returns
-------
fig, ax : Matplotlib instances.
The figure and axe instances.
"""
ax = plot_hr(self, ax=ax)
return ax
# Add ectobeat (type 2) rr[80] = 500 rr[81] = 1100 #%% # Artefact detection # ------------------ # You can visualize the two main subspaces and spot outliers. # Here we can see that two intervals have been labelled as probable ectobeats # (left pannel), and a total of 6 datapoints are considered as outliers, being # too long or too short (right pannel). plot_subspaces(rr) #%% # Plotting # -------- # We can then plot back the labelled outliers in the RR interval time course ectobeats, outliers = rr_outliers(rr) plot_hr(rr.values, kind='linear', outliers=(ectobeats | outliers)) #%% # References # ---------- # .. [#] 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