from ..signal import signal_filter
[docs]
def ecg_rsp(ecg_rate, sampling_rate=1000, method="vangent2019"):
"""**ECG-Derived Respiration (EDR)**
Extract ECG-Derived Respiration (EDR), a proxy of a respiratory signal based on heart rate.
Different methods include:
* **vangent2019**: 0.1-0.4 Hz filter.
* **soni2019**: 0-0.5 Hz filter.
* **charlton2016**: 0.066-1 Hz filter.
* **sarkar2015**: 0.1-0.7 Hz filter.
.. warning::
Help is required to double-check whether the implementation match the papers.
Parameters
----------
ecg_rate : array
The heart rate signal as obtained via ``ecg_rate()``.
sampling_rate : int
The sampling frequency of the signal that contains the R-peaks (in Hz,
i.e., samples/second). Defaults to 1000Hz.
method : str
Can be one of ``"vangent2019"`` (default), ``"soni2019"``, ``"charlton2016"`` or
``"sarkar2015"``.
Returns
-------
array
A Numpy array containing the derived respiratory rate.
Examples
--------
* **Example 1:** Compare to real RSP signal
.. ipython:: python
import neurokit2 as nk
# Get heart rate
data = nk.data("bio_eventrelated_100hz")
rpeaks, info = nk.ecg_peaks(data["ECG"], sampling_rate=100)
ecg_rate = nk.signal_rate(rpeaks, sampling_rate=100, desired_length=len(rpeaks))
# Get ECG Derived Respiration (EDR) and add to the data
data["EDR"] = nk.ecg_rsp(ecg_rate, sampling_rate=100)
# Visualize result
@savefig p_ecg_rsp1.png scale=100%
nk.signal_plot([data["RSP"], data["EDR"]], standardize = True)
@suppress
plt.close()
* **Example 2:** Methods comparison
.. ipython:: python
data["vangent2019"] = nk.ecg_rsp(ecg_rate, sampling_rate=100, method="vangent2019")
data["sarkar2015"] = nk.ecg_rsp(ecg_rate, sampling_rate=100, method="sarkar2015")
data["charlton2016"] = nk.ecg_rsp(ecg_rate, sampling_rate=100, method="charlton2016")
data["soni2019"] = nk.ecg_rsp(ecg_rate, sampling_rate=100, method="soni2019")
# Visualize results
@savefig p_ecg_rsp2.png scale=100%
nk.signal_plot([data["RSP"], data["vangent2019"], data["sarkar2015"],
data["charlton2016"], data["soni2019"]], standardize = True)
@suppress
plt.close()
References
----------
* van Gent, P., Farah, H., van Nes, N., & van Arem, B. (2019). HeartPy: A novel heart rate
algorithm for the analysis of noisy signals. Transportation research part F: traffic
psychology and behaviour, 66, 368-378.
* Sarkar, S., Bhattacherjee, S., & Pal, S. (2015). Extraction of respiration signal from ECG for
respiratory rate estimation.
* Charlton, P. H., Bonnici, T., Tarassenko, L., Clifton, D. A., Beale, R., & Watkinson, P. J.
(2016). An assessment of algorithms to estimate respiratory rate from the electrocardiogram
and photoplethysmogram. Physiological measurement, 37(4), 610.
* Soni, R., & Muniyandi, M. (2019). Breath rate variability: a novel measure to study the
meditation effects. International Journal of Yoga, 12(1), 45.
"""
# TODO: It would be interesting to run a study in which we modulate the different filtering
# parameters and compute the difference with the real RSP signal, and then suggest the optimal
# filtering parameters. If you're interested in helping out let us know!
method = method.lower()
if method in ["sarkar2015"]:
# https://www.researchgate.net/publication/304221962_Extraction_of_respiration_signal_from_ECG_for_respiratory_rate_estimation # noqa: E501
rsp = signal_filter(ecg_rate, sampling_rate, lowcut=0.1, highcut=0.7, order=6)
elif method in ["charlton2016"]:
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390977/#__ffn_sectitle
rsp = signal_filter(ecg_rate, sampling_rate, lowcut=4 / 60, highcut=60 / 60, order=6)
elif method in ["soni2019"]:
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329220/
rsp = signal_filter(ecg_rate, sampling_rate, highcut=0.5, order=6)
elif method in ["vangent2019"]:
# https://github.com/paulvangentcom/heartrate_analysis_python/blob/1597e8c0b2602829428b22d8be88420cd335e939/heartpy/analysis.py#L541 # noqa: E501
rsp = signal_filter(ecg_rate, sampling_rate, lowcut=0.1, highcut=0.4, order=2)
else:
raise ValueError(
"`method` should be one of 'sarkar2015', 'charlton2016', 'soni2019' or "
"'vangent2019'."
)
return rsp
