def calculate_hrv_features(rri, f=360):
"""
Calculate features for detecting sleep apnea
The features contain statistical measures, morphology and periodogram
of the ECG signal.
Parameters
----------
RR_int: array_like
Array containing RR intervals
f: float, optional
Number corresponding to the sampling frequency of the input signal,
must be in Hertz
Returns
-------
X: array_like
Features
"""
result_td = np.reshape(np.asarray(list(time_domain(rri).values())), (1, 6))
result_fd = np.reshape(np.asarray(list(frequency_domain(rri).values())),
(1, 7))
result_nl = np.reshape(np.asarray(list(non_linear(rri).values())), (1, 2))
hrv_features = np.concatenate([result_td, result_fd, result_nl], axis=1)
return hrv_features
def poincare_plot(self):
"""
Poincaré plot of the RRi series
"""
fig, ax = plt.subplots(1, 1)
rri_n, rri_n_1 = self.rri[:-1], self.rri[1:]
ax.plot(rri_n, rri_n_1, '.k')
ax.set(
xlabel='$RRi_n$ (ms)',
ylabel='$RRi_{n+1}$ (ms)',
title='Poincaré Plot'
)
plt.show(block=False)
# The ellipse drawning is a translation from the Matlab code
# available at: https://github.com/jramshur/HRVAS
dx = abs(max(rri_n) - min(rri_n)) * 0.05
dy = abs(max(rri_n_1) - min(rri_n_1)) * 0.05
xlim = [min(rri_n) - dx, max(rri_n) + dx]
ylim = [min(rri_n_1) - dy, max(rri_n_1) + dy]
from hrv.classical import non_linear
nl = non_linear(self.rri)
a = rri_n / np.cos(np.pi/4.0)
ca = np.mean(a)
cx, cy, _ = ca * np.cos(np.pi/4.0), ca * np.sin(np.pi/4.0), 0
width = nl['sd2'] # to seconds
height = nl['sd1'] # to seconds
# plot fx(x) = x
sd2_l = ax.plot(
[xlim[0], xlim[1]],
[ylim[0], ylim[1]],
'--', color=[0.5, 0.5, 0.5]
)
fx = lambda val: -val + 2 * cx
sd1_l = ax.plot([xlim[0], xlim[1]], [fx(xlim[0]), fx(xlim[1])], 'k--')
ax = _ellipsedraw(
ax,
width,
height,
cx,
cy,
np.pi/4.0,
color='r',
linewidth=3
)
ax.legend(
(sd1_l[0], sd2_l[0]),
('SD1: %.2fms' % height, 'SD2: %.2fms' % width),
)
return fig, ax
def get_hrv(rr_interval):
"""Get three domain heart rate variability.
Get time domain, frequency domain, and non-linear domain heart rate variability.
Args:
rr_interval: narray, RR-interval.
Returns:
A dictionary representation of the three domain HRV.
Notes:
*Authors*
- the hrv dev team (https://github.com/rhenanbartels/hrv)
*Dependencies*
- hrv
- numpy
*See Also*
- hrv: https://github.com/rhenanbartels/hrv
"""
if np.median(rr_interval) < 1:
rr_interval *= 1000
time_domain_analysis = time_domain(rr_interval)
frequency_domain_analysis = frequency_domain(rri=rr_interval,
fs=4.0,
method='welch',
interp_method='cubic',
detrend='linear')
non_linear_domain_analysis = non_linear(rr_interval)
hrv_info = {
'time': time_domain_analysis,
'frequency': frequency_domain_analysis,
'non-linear': non_linear_domain_analysis
}
return hrv_info
from hrv.filters import moving_median
import numpy as np
# def _moving_function(rri, order, func):
# offset = int(order / 2)
# filt_rri = np.array(rri.copy(), dtype=np.float64)
# for i in range(offset, len(rri) - offset, 1):
# filt_rri[i] = func(rri[i-offset:i+offset+1])
# return filt_rri
# def moving_median(rri, order=3):
# return _moving_function(rri, order, np.median)
rri = open_rri('./data/test/extracted/Iris_kangxi_peyin_RRI.txt')
filt_rri = moving_median(rri, order=3)
results = time_domain(filt_rri)
print(results)
results = frequency_domain(rri=filt_rri,
fs=4.0,
method='welch',
interp_method='cubic',
detrend='linear')
print(results)
results = non_linear(filt_rri)
print(results)
# change it depending on which one you want output
specifier = 'td'
# this script is GENERAL purpose. you need to change some of these to match the filenames
for i in range(1, 55): # change range
filename_header = 'nsr' + str(i).zfill(3) # change name possibly
filename = filename_header + 'rri.txt'
classifier = ''
try:
rri = open_rri(filename)
except:
continue
if specifier == 'td':
results = time_domain(rri) # output is dict
elif specifier == 'nl':
results = non_linear(rri)
else:
print("Invalid argument!")
output_csv_name = filename_header + 'hrv_' + specifier + '.csv'
with open(output_csv_name, 'wb') as f: # Just use 'w' mode in 3.x
w = csv.DictWriter(f, results.keys())
w.writeheader()
w.writerow(results)
print(filename_header + ' rri file has been processed')
""" print(results)
print(type(results)) """