# -*- coding: utf-8 -*-
import numpy as np
from ..misc.report import get_kwargs
from .ppg_clean import ppg_clean
from .ppg_findpeaks import ppg_findpeaks
[docs]
def ppg_methods(
sampling_rate=1000,
method="elgendi",
method_cleaning="default",
method_peaks="default",
**kwargs,
):
"""**PPG Preprocessing Methods**
This function analyzes and specifies the methods used in the preprocessing, and create a
textual description of the methods used. It is used by :func:`ppg_process()` to dispatch the
correct methods to each subroutine of the pipeline and :func:`ppg_report()` to create a
preprocessing report.
Parameters
----------
sampling_rate : int
The sampling frequency of the raw PPG signal (in Hz, i.e., samples/second).
method : str
The method used for cleaning and peak finding if ``"method_cleaning"``
and ``"method_peaks"`` are set to ``"default"``. Can be one of ``"elgendi"``.
Defaults to ``"elgendi"``.
method_cleaning: str
The method used to clean the raw PPG signal. If ``"default"``,
will be set to the value of ``"method"``. Defaults to ``"default"``.
For more information, see the ``"method"`` argument
of :func:`.ppg_clean`.
method_peaks: str
The method used to find peaks. If ``"default"``,
will be set to the value of ``"method"``. Defaults to ``"default"``.
For more information, see the ``"method"`` argument
of :func:`.ppg_findpeaks`.
**kwargs
Other arguments to be passed to :func:`.ppg_clean` and
:func:`.ppg_findpeaks`.
Returns
-------
report_info : dict
A dictionary containing the keyword arguments passed to the cleaning
and peak finding functions, text describing the methods, and the corresponding
references.
See Also
--------
ppg_process, ppg_clean, ppg_findpeaks
Examples
--------
.. ipython:: python
import neurokit2 as nk
methods = nk.ppg_methods(sampling_rate=100, method="elgendi", method_cleaning="nabian2018")
print(methods["text_cleaning"])
print(methods["references"][0])
"""
# Sanitize inputs
method_cleaning = (
str(method).lower()
if method_cleaning == "default"
else str(method_cleaning).lower()
)
method_peaks = (
str(method).lower() if method_peaks == "default" else str(method_peaks).lower()
)
# Create dictionary with all inputs
report_info = {
"sampling_rate": sampling_rate,
"method": method,
"method_cleaning": method_cleaning,
"method_peaks": method_peaks,
**kwargs,
}
# Get arguments to be passed to cleaning and peak finding functions
kwargs_cleaning, report_info = get_kwargs(report_info, ppg_clean)
kwargs_peaks, report_info = get_kwargs(report_info, ppg_findpeaks)
# Save keyword arguments in dictionary
report_info["kwargs_cleaning"] = kwargs_cleaning
report_info["kwargs_peaks"] = kwargs_peaks
# Initialize refs list with NeuroKit2 reference
refs = ["""Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H.,
Schölzel, C., & Chen, S. A. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing.
Behavior Research Methods, 53(4), 1689–1696. https://doi.org/10.3758/s13428-020-01516-y
"""]
# 1. Cleaning
# ------------
report_info["text_cleaning"] = f"The raw signal, sampled at {sampling_rate} Hz,"
if method_cleaning in [
"elgendi",
"elgendi2013",
]:
report_info["text_cleaning"] += (
" was preprocessed using a bandpass filter ([0.5 - 8 Hz], Butterworth 3rd order;"
+ " following Elgendi et al., 2013)."
)
refs.append(
"""Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D (2013)
Systolic Peak Detection in Acceleration Photoplethysmograms
Measured from Emergency Responders in Tropical Conditions
PLoS ONE 8(10): e76585. doi:10.1371/journal.pone.0076585."""
)
elif method_cleaning in ["nabian", "nabian2018"]:
if report_info["heart_rate"] is None:
cutoff = "of 40 Hz"
else:
cutoff = f' based on the heart rate of {report_info["heart_rate"]} bpm'
report_info["text_cleaning"] = (
f" was preprocessed using a lowpass filter (with a cutoff frequency {cutoff},"
+ " butterworth 2nd order; following Nabian et al., 2018)."
)
refs.append(
"""Nabian, M., Yin, Y., Wormwood, J., Quigley, K. S., Barrett, L. F., & Ostadabbas, S.(2018).
An open-source feature extraction tool for the analysis of peripheral physiological data.
IEEE Journal of Translational Engineering in Health and Medicine, 6, 1-11."""
)
elif method_cleaning in ["none"]:
report_info[
"text_cleaning"
] += " was directly used for peak detection without preprocessing."
else:
# just in case more methods are added
report_info["text_cleaning"] = (
"was cleaned following the " + method + " method."
)
# 2. Peaks
# ----------
if method_peaks in ["elgendi", "elgendi13"]:
report_info[
"text_peaks"
] = "The peak detection was carried out using the method described in Elgendi et al. (2013)."
refs.append(
"""Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D (2013)
Systolic Peak Detection in Acceleration Photoplethysmograms
Measured from Emergency Responders in Tropical Conditions
PLoS ONE 8(10): e76585. doi:10.1371/journal.pone.0076585."""
)
elif method_peaks in ["none"]:
report_info["text_peaks"] = "There was no peak detection carried out."
else:
report_info[
"text_peaks"
] = f"The peak detection was carried out using the method {method_peaks}."
report_info["references"] = list(np.unique(refs))
return report_info
