@@ -52,10 +52,10 @@ def oxi_peaks(x, sfreq=75, win=1, new_sfreq=1000, clipping=True,
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References
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----------
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- Some of the processing steps were adapted from the HeartPy toolbox [1] :
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+ Some of the processing steps were adapted from the HeartPy toolbox:
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https://python-heart-rate-analysis-toolkit.readthedocs.io/en/latest/index.html
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- ..[1] van Gent, P., Farah, H., van Nes, N. and van Arem, B., 2019.
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+ .. [1] van Gent, P., Farah, H., van Nes, N. and van Arem, B., 2019.
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Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors:
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Heart Rate Analysis Software from the Taking the Fast Lane Project. Journal
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of Open Research Software, 7(1), p.32. DOI: http://doi.org/10.5334/jors.241
@@ -111,6 +111,8 @@ def ecg_peaks(x, sfreq=1000, new_sfreq=1000, method='pan-tompkins',
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find_local = True , win_size = 100 ):
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"""A simple wrapper for many popular R peaks detectors algorithms.
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+ This function calls methods from the py-ecg-detectors [#]_ module.
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+
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Parameters
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----------
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x : list or 1d array-like
@@ -152,10 +154,7 @@ def ecg_peaks(x, sfreq=1000, new_sfreq=1000, method='pan-tompkins',
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References
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----------
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- Some of the processing steps were adapted from the HeartPy toolbox [1]:
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- https://python-heart-rate-analysis-toolkit.readthedocs.io/en/latest/index.html
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-
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- ..[1] Howell, L., Porr, B. Popular ECG R peak detectors written in
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+ .. [#] Howell, L., Porr, B. Popular ECG R peak detectors written in
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python. DOI: 10.5281/zenodo.3353396
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"""
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if isinstance (x , list ):
@@ -247,8 +246,8 @@ def rr_artefacts(rr, c1=0.13, c2=0.17, alpha=5.2):
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Notes
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-----
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- This function will use the method proposed by Lipponen & Tarvainen (2019)
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- to detect ectopic beats, long, shorts, missed and extra RR intervals.
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+ This function will use the method proposed by [#]_ to detect ectopic beats,
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+ long, shorts, missed and extra RR intervals.
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Examples
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--------
@@ -260,7 +259,7 @@ def rr_artefacts(rr, c1=0.13, c2=0.17, alpha=5.2):
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References
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----------
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- .. [1 ] Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for
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+ .. [# ] Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for
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heart rate variability time series artefact correction using novel
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beat classification. Journal of Medical Engineering & Technology,
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43(3), 173–181. https://doi.org/10.1080/03091902.2019.1640306
@@ -369,7 +368,10 @@ def rr_artefacts(rr, c1=0.13, c2=0.17, alpha=5.2):
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def interpolate_clipping (signal , threshold = 255 ):
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- """Interoplate clipping segment.
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+ """Interoplate clipping artefacts.
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+
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+ This function removes all data points equalling the provided threshold
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+ and re-creates the missing segments using cubic spline interpolation.
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Parameters
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----------
@@ -405,7 +407,7 @@ def interpolate_clipping(signal, threshold=255):
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.. Warning:: If clipping artefact is found at the edge of the signal, this
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function will decrement the first/last value to allow interpolation,
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- which can lead to incorrect estimation.
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+ which can result in incorrect estimation.
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References
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----------
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