Add EEG-HRV coupling notebook (#89)

* Added EEG-HRV coupling notebook

* Minor updates

README.rst

@@ -103,6 +103,7 @@ The notebooks and datasets can be found on `GitHub <https://github.com/raphaelva
 * `spectrogram <notebooks/10_spectrogram.ipynb>`_: plot a multi-taper full-night spectrogram on single-channel EEG data with the hypnogram on top.
 * `nonlinear_features <notebooks/11_nonlinear_features.ipynb>`_: calculate non-linear EEG features on 30-seconds epochs and perform a naive sleep stage classification.
 * `SO-sigma_coupling <notebooks/12_SO-sigma_coupling.ipynb>`_: slow-oscillations/spindles phase-amplitude coupling and data-driven comodulogram.
+* `EEG-HRV coupling <notebooks/16_EEG-HRV_coupling.ipynb>`_: overnight coupling between EEG bandpower and heart rate variability.
 * `topoplot <notebooks/15_topoplot.ipynb>`_: topoplot.
 
 Gallery

docs/changelog.rst

@@ -16,6 +16,7 @@ v0.6.2.dev
 a. Added the :py:func:`yasa.hypno_find_periods` function to find sequences of consecutive values in hypnogram that are longer than a certain duration. This is a flexible function that can be used to detect NREM/REM periods.
 b. Added the :py:func:`yasa.hrv_stage` function, which calculates heart rate (HR) and heart rate variability (HRV) by stage and periods.
 c. Added a new dataset containing 8 hours of ECG data. The dataset is in compressed NumPy format and can be found in notebooks/data_ECG_8hrs_200Hz.npz. The dataset also includes an upsampled hypnogram.
+d. Added a new Jupyter notebook to calculate EEG-HRV overnight coupling based on the :py:func:`yasa.hrv_stage` function. https://github.com/raphaelvallat/yasa/blob/master/notebooks/16_EEG-HRV_coupling.ipynb
 
 **Spindles & slow-waves detection** - `PR 71 <https://github.com/raphaelvallat/yasa/pull/71>`_
 

docs/index.rst

@@ -105,6 +105,7 @@ The notebooks and datasets can be found on `GitHub <https://github.com/raphaelva
 * `spectrogram <https://github.com/raphaelvallat/yasa/blob/master/notebooks/10_spectrogram.ipynb>`_: plot a multi-taper full-night spectrogram on single-channel EEG data with the hypnogram on top.
 * `nonlinear_features <https://github.com/raphaelvallat/yasa/blob/master/notebooks/11_nonlinear_features.ipynb>`_: calculate non-linear EEG features on 30-seconds epochs and perform a naive sleep stage classification.
 * `SO-sigma_coupling <https://github.com/raphaelvallat/yasa/blob/master/notebooks/12_SO-sigma_coupling.ipynb>`_: slow-oscillations/spindles phase-amplitude coupling and data-driven comodulogram.
+* `EEG-HRV coupling <https://github.com/raphaelvallat/yasa/blob/master/notebooks/16_EEG-HRV_coupling.ipynb>`_: overnight coupling between EEG bandpower and heart rate variability.
 * `topoplot <https://github.com/raphaelvallat/yasa/blob/master/notebooks/15_topoplot.ipynb>`_: topoplot.
 
 **********

yasa/heart.py

@@ -109,6 +109,9 @@ def hrv_stage(
 
     Heartbeat detection is performed with the SleepECG library: https://github.com/cbrnr/sleepecg
 
+    For an example of this function, please see the `Jupyter notebook
+    <https://github.com/raphaelvallat/yasa/blob/master/notebooks/16_EEG-HRV_coupling.ipynb>`_
+
     References
     ----------
     * Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and