Add Hilbert RVT (Harrison et al. 2020)#22
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| # not sure why pi is here but it's in the preprint's formula | ||
| ibr = np.append(0, np.diff(phas)) / (2 * np.pi) |
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Per @CesarCaballeroGaudes, this seems to normalize radians (see #10 (comment)).
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| # low-pass filter both(?) at 0.2Hz | ||
| ibr = butter_bandpass_filter(ibr, fs=sampling_freq, highcut=0.75) | ||
| rv = butter_bandpass_filter(rv, fs=sampling_freq, highcut=0.75) |
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I'm not sure if both signals should be low-pass filtered here, or if just one should be.
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| # Reconstruct the oscillatory portion of the signal, cos(𝜙(𝑡)), | ||
| # and lowpass filter at 0.75 Hz to remove any resulting artefacts. | ||
| oscill = np.cos(phas) | ||
| oscill = butter_bandpass_filter(oscill, fs=sampling_freq, highcut=0.75) | ||
| phas = np.arccos(oscill) |
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It seems to me that a number of transformations could be done here, and I'm not sure why one was chosen over the others:
- phase to oscillatory portion of signal (as is done)
- phase + magnitude to analytic signal, to be split again after low-pass filtering (assuming
filtfiltcan work with complex-valued data). - phase + magnitude to reconstructed original signal, to be transformed again using the Hilbert after low-pass filtering.
- Just filtering the phase signal, right?
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Dec 31, 2020
| from scipy import signal | ||
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| def get_butter_filter(fs, lowcut=None, highcut=None, order=5): |
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Is a Butterworth filter (+ filtfilt) the way to go?
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Closes #10.
This adds a new function (
chest_belt.hilbert_rvt()) to calculate the Hilbert transform-based RVT metric from Harrison et al. (2020). This implementation is based on the manuscript, rather than any available code (couldn't find any).Proposed Changes
metrics.chest_belt.hilbert_rvt()to calculate the new metric.metrics.chest_belt.utilsmodule in support of the new metric.Change Type
bugfix(+0.0.1)minor(+0.1.0)major(+1.0.0)refactoring(no version update)test(no version update)infrastructure(no version update)documentation(no version update)otherChecklist before review