CFT Example

This example illustrates how to process data collected when conducting the Cold Face Test (CFT) procedure, i.e., how to compute parameters quantifying the reaction to the CFT and how to create specialized CFT plots.

Setup and Helper Functions

from pathlib import Path

import re

import pandas as pd
import numpy as np

from fau_colors import cmaps

import biopsykit as bp
from biopsykit.protocols import CFT

import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline
%load_ext autoreload
%autoreload 2

plt.close("all")

palette = sns.color_palette(cmaps.faculties)
sns.set_theme(context="notebook", style="ticks", font="sans-serif", palette=palette)

plt.rcParams["figure.figsize"] = (8, 4)
plt.rcParams["pdf.fonttype"] = 42
plt.rcParams["mathtext.default"] = "regular"

palette

Create CFT Object

The CFT is a protocol and thus part of the Protocol API of BioPsyKit.

cft = CFT()
cft

CFT
        Structure: {'Baseline': 60, 'CFT': 120, 'Recovery': 60}

Load Data

hr_dict = bp.example_data.get_mist_hr_example()

# Alternatively: Use your own data
# hr_dict = bp.io.ecg.load_hr_phase_dict("<path-to-heart-rate-data-dictionary>")
df_hr = hr_dict["MIST3"]

df_hr.head()

	Heart_Rate
time
2019-10-22 11:53:08.977000+02:00	101.721854
2019-10-22 11:53:10.164000+02:00	100.392157
2019-10-22 11:53:10.762000+02:00	99.740260
2019-10-22 11:53:11.363000+02:00	101.052632
2019-10-22 11:53:11.957000+02:00	102.400000

Plot Course of Heart Rate

fig, ax = plt.subplots()
bp.signals.ecg.plotting.hr_plot(df_hr, ax=ax)
fig.tight_layout()

Note: See plotting.hr_plot() for further information!

Compute CFT Parameter

Extract CFT Interval, i.e., the interval of the data where the Cold Face stimulus was actually applied (corresponds to the duration of the “CFT” phase specified in the CFT object) and compute baseline heart rate (the mean heart rate during the “Baseline” phase specified in the CFT object).

Note: See CFT.extract_cft_interval() and CFT.compute_cft_parameter() for further information!

df_cft = cft.extract_cft_interval(df_hr)
bl = cft.baseline_hr(df_hr)

print("Baseline Heart Rate: {:.2f}".format(bl))
df_cft.head()

Baseline Heart Rate: 86.57

	Heart_Rate
time
2019-10-22 11:54:09.547000+02:00	93.090909
2019-10-22 11:54:10.191000+02:00	91.428571
2019-10-22 11:54:10.848000+02:00	94.814815
2019-10-22 11:54:11.480000+02:00	97.215190
2019-10-22 11:54:12.098000+02:00	97.834395

cft.compute_cft_parameter(df_hr)

	baseline_hr	cft_start_idx	onset	onset_latency	onset_idx	onset_hr	onset_hr_percent	onset_slope	peak_brady	peak_brady_latency	peak_brady_idx	peak_brady_bpm	peak_brady_percent	peak_brady_slope	mean_hr_bpm	mean_brady_bpm	mean_brady_percent	poly_fit_a0	poly_fit_a1	poly_fit_a2
0	86.57039	86	2019-10-22 11:54:16.852000+02:00	7.305	11	80.0	7.589651	-0.899437	2019-10-22 11:54:36.980000+02:00	27.433	38	-14.457714	-16.70053	-0.527019	82.25004	-4.32035	-4.990563	86.823415	-0.230385	0.001917

Plot CFT Data

fig, ax = plt.subplots()

cft.cft_plot(data=df_hr, ax=ax)
fig.tight_layout()

Note: See CFT.cft_plot() for further information!

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