# -*- coding: utf-8 -*-
import numpy as np
import scipy.spatial
from ..complexity import complexity_rqa
from ..signal import signal_detrend
from .hrv_utils import _hrv_format_input
[docs]
def hrv_rqa(
peaks,
sampling_rate=1000,
dimension=7,
delay=1,
tolerance="zimatore2021",
show=False,
**kwargs,
):
"""**Recurrence Quantification Analysis (RQA) of Heart Rate Variability (HRV)**
RQA is a type of complexity analysis used in non-linear dynamics (related to entropy and fractal
dimensions). See :func:`.complexity_rqa` for more information.
Parameters
----------
peaks : dict
Samples at which cardiac extrema (i.e., R-peaks, systolic peaks) occur.
Can be a list of indices or the output(s) of other functions such as :func:`.ecg_peaks`,
:func:`.ppg_peaks`, :func:`.ecg_process` or :func:`.bio_process`.
sampling_rate : int, optional
Sampling rate (Hz) of the continuous cardiac signal in which the peaks occur. Should be at
least twice as high as the highest frequency in vhf. By default 1000.
delay : int
See :func:`.complexity_rqa` for more information.
dimension : int
See :func:`.complexity_rqa` for more information.
tolerance : float
See :func:`.complexity_rqa` for more information. If ``"zimatore2021"``, will be set to half
of the mean pairwise distance between points.
show : bool
See :func:`.complexity_rqa` for more information.
**kwargs
Other arguments to be passed to :func:`.complexity_rqa`.
See Also
--------
complexity_rqa, hrv_nonlinear
Returns
----------
rqa : float
The RQA.
Examples
--------
.. ipython:: python
import neurokit2 as nk
# Download data
data = nk.data("bio_resting_5min_100hz")
# Find peaks
peaks, info = nk.ecg_peaks(data["ECG"], sampling_rate=100)
# Compute HRV RQA indices
@savefig p_hrv_rqa1.png scale=100%
hrv_rqa = nk.hrv_rqa(peaks, sampling_rate=100, show=True)
@suppress
plt.close()
.. ipython:: python
hrv_rqa
References
----------
* Zimatore, G., Falcioni, L., Gallotta, M. C., Bonavolontà, V., Campanella, M., De Spirito, M.,
... & Baldari, C. (2021). Recurrence quantification analysis of heart rate variability to
detect both ventilatory thresholds. PloS one, 16(10), e0249504.
* Ding, H., Crozier, S., & Wilson, S. (2008). Optimization of Euclidean distance threshold in
the application of recurrence quantification analysis to heart rate variability studies.
Chaos, Solitons & Fractals, 38(5), 1457-1467.
"""
# Sanitize input
# If given peaks, compute R-R intervals (also referred to as NN) in milliseconds
rri, _, _ = _hrv_format_input(peaks, sampling_rate=sampling_rate)
# Linear detrend (Zimatore, 2021)
rri = signal_detrend(rri, method="polynomial", order=1)
# Radius (50% of mean distance between all pairs of points in time)
if tolerance == "zimatore2021":
dists = scipy.spatial.distance.pdist(np.array([rri, rri]).T, "euclidean")
tolerance = 0.5 * np.mean(dists)
# Run the RQA
rqa, _ = complexity_rqa(
rri,
dimension=dimension,
delay=delay,
tolerance=tolerance,
show=show,
**kwargs,
)
return rqa
