Few remarks from a first-timer about systole

[LegrandNico]

Hi @ryobg , thank you for pointing this out, this kind of feedback is really helpful for developing the library 👍

These are all good points, I will work on a PR to add some fixes for that. Regarding the wavelet approach, this is probably an issue in itself. Do you have papers or toolbox that document that as a starting point?

[ryobg]

Heya, wrt to the wavelets:

I have checked out few articles:

• Wavelet transform to quantify heart rate variability and to assess its instantaneous changes
  https://journals.physiology.org/doi/full/10.1152/jappl.1999.86.3.1081

• Wavelet analysis of heart rate variability: Impact of wavelet selection
  https://www.researchgate.net/publication/320555701_Wavelet_analysis_of_heart_rate_variability_Impact_of_wavelet_selection

• Spectral Analysis of Heart Rate Variability: Time Window Matters
  https://www.frontiersin.org/articles/10.3389/fneur.2019.00545/full

But there are many others which are behind a paywall. My non-physiology view says that the ability to look at the temporal changes of cycles over time is a good addition. It seems, many articles agrees on that. The difficulty is being to setup the proper transforms of the mother wavelet (Daubechies?). I'm currently investigating some wavelet libs in python, like scipy.signal.cwt and PyWavelets.

I'm sorry I'm unable to help here.

[ryobg]

I'm having some barely noticeable success with a code along the lines of:

import pywt
wavelet = 'morl'
periods = [2.5, 5, 10, 20, 40, 80, 160, 320]
scales = pywt.frequency2scale (wavelet, 1 / numpy.array (periods))
_, ax = plt.subplots (figsize = (20, 7))
ax.set_ylabel ("Frequency (s)")
ax.set_xlabel ("Time (minutes)")
coef, _ = pywt.cwt (ibi, scales, wavelet)
ax.imshow (abs (coef), aspect = 'auto', interpolation = 'nearest',
        extent = [0, ibi.sum () / (1000*60), max (periods), min (periods)],
        vmax = abs (coef).max (), vmin = abs (coef).min ())  
plt.show ()