def test_moving_median_order_5(self):
fake_rri = np.array([810, 830, 860, 790, 804, 801, 800])
rri_filt = moving_median(fake_rri, order=5)
expected = [810, 830, 810.0, 804.0, 801.0, 801, 800]
np.testing.assert_almost_equal(rri_filt, expected, decimal=2)
def test_moving_median_oder_3(self):
fake_rri = np.array([810, 830, 860, 790, 804])
rri_filt = moving_median(fake_rri, order=3)
expected = [810, 830.0, 830.0, 804, 804]
assert isinstance(rri_filt, RRi)
np.testing.assert_almost_equal(rri_filt.values, expected, decimal=2)
def test_movinng_filters_receiving_and_return_rri_class(self):
fake_rri = RRi([810, 830, 860, 790, 804, 801, 800], time=[0, 1, 2, 3, 4, 5, 6])
rri_filt = moving_median(fake_rri)
expected_rri = [810, 830, 830, 804, 801, 801, 800]
expected_time = [0, 1, 2, 3, 4, 5, 6]
assert isinstance(rri_filt, RRi)
np.testing.assert_almost_equal(rri_filt.values, expected_rri, decimal=2)
np.testing.assert_almost_equal(rri_filt.time, expected_time, decimal=2)
qrs_ann = wfdb.rdann(data_path + train_data_name[data_index],
'qrs',
sampfrom=samp_from - (MARGIN * 100),
sampto=samp_to + (MARGIN * 100)).sample
apn_ann = wfdb.rdann(data_path + train_data_name[data_index],
'apn',
sampfrom=samp_from,
sampto=samp_to - 1).symbol
qrs_amp = get_qrs_amp(signals, qrs_ann)
rri = np.diff(qrs_ann)
rri_ms = rri.astype('float') / FS * 1000.0
try:
rri_filt = moving_median(rri_ms)
if len(rri_filt) > 5 and (np.min(rri_filt) >= MIN_RRI
and np.max(rri_filt) <= MAX_RRI):
time_intp, rri_intp = interp_cubic_spline(rri_filt, FS_INTP)
qrs_time_intp, qrs_intp = interp_cubic_spline_qrs(
qrs_ann, qrs_amp, FS_INTP)
rri_intp = rri_intp[(time_intp >= MARGIN)
& (time_intp < (60 + MARGIN))]
qrs_intp = qrs_intp[(qrs_time_intp >= MARGIN)
& (qrs_time_intp < (60 + MARGIN))]
#time_intp = time_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
if len(rri_intp) != (FS_INTP * 60):
skip = 1
else:
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)
qrs_ann = wfdb.rdann(data_path + train_data_name[data_index],
'qrs',
sampfrom=samp_from - (MARGIN * 100),
sampto=samp_to + (MARGIN * 100)).sample
apn_ann = wfdb.rdann(data_path + train_data_name[data_index],
'apn',
sampfrom=samp_from,
sampto=samp_to - 1).symbol
qrs_amp = get_qrs_amp(signals, qrs_ann)
rri = np.diff(qrs_ann)
rri_ms = rri.astype('float') / FS * 1000.0
rri_filt = moving_median(rri_ms)
qrs_filt = moving_median(qrs_amp)
if rri_filt.shape[0] > 5 and (np.min(rri_filt) >= MIN_RRI
and np.max(rri_filt) <= MAX_RRI):
time_intp, rri_intp = interp_cubic_spline(rri_filt, FS_INTP)
qrs_time_intp, qrs_intp = interp_cubic_spline_qrs(
qrs_ann, qrs_amp, FS_INTP)
rri_intp = rri_intp[(time_intp >= MARGIN)
& (time_intp < (60 + MARGIN))]
qrs_intp = qrs_intp[(qrs_time_intp >= MARGIN)
& (qrs_time_intp < (60 + MARGIN))]
#time_intp = time_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
if len(rri_intp) != (FS_INTP * 60):
skip = 1
else:
def apnea_preproc():
import wfdb
import matplotlib.pyplot as plt
import numpy as np
from hrv.filters import quotient, moving_median
from scipy import interpolate
from tqdm import tqdm
import os
FS = 100.0
# From https://github.com/rhenanbartels/hrv/blob/develop/hrv/classical.py
def create_time_info(rri):
rri_time = np.cumsum(rri) / 1000.0 # make it seconds
return rri_time - rri_time[0] # force it to start at zero
def create_interp_time(rri, fs):
time_rri = create_time_info(rri)
return np.arange(0, time_rri[-1], 1 / float(fs))
def interp_cubic_spline(rri, fs):
time_rri = create_time_info(rri)
time_rri_interp = create_interp_time(rri, fs)
tck = interpolate.splrep(time_rri, rri, s=0)
rri_interp = interpolate.splev(time_rri_interp, tck, der=0)
return time_rri_interp, rri_interp
def interp_cubic_spline_qrs(qrs_index, qrs_amp, fs):
time_qrs = qrs_index / float(FS)
time_qrs = time_qrs - time_qrs[0]
time_qrs_interp = np.arange(0, time_qrs[-1], 1 / float(fs))
tck = interpolate.splrep(time_qrs, qrs_amp, s=0)
qrs_interp = interpolate.splev(time_qrs_interp, tck, der=0)
return time_qrs_interp, qrs_interp
data_path = f'{DATASET_DIR}/apnea/'
train_data_name = [
'a01',
'a02',
'a03',
'a04',
'a05',
'a06',
'a07',
'a08',
'a09',
'a10',
'a11',
'a12',
'a13',
'a14',
'a15',
'a16',
'a17',
'a18',
'a19',
'b01',
'b02',
'b03',
'b04',
'c01',
'c02',
'c03',
'c04',
'c05',
'c06',
'c07',
'c08',
'c09',
]
test_data_name = ['a20', 'b05', 'c10']
age = [
51, 38, 54, 52, 58, 63, 44, 51, 52, 58, 58, 52, 51, 51, 60, 44, 40, 52,
55, 58, 44, 53, 53, 42, 52, 31, 37, 39, 41, 28, 28, 30, 42, 37, 27
]
sex = [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1
]
def get_qrs_amp(ecg, qrs):
interval = int(FS * 0.250)
qrs_amp = []
for index in range(len(qrs)):
curr_qrs = qrs[index]
amp = np.max(ecg[curr_qrs - interval:curr_qrs + interval])
qrs_amp.append(amp)
return qrs_amp
MARGIN = 10
FS_INTP = 4
MAX_HR = 300.0
MIN_HR = 20.0
MIN_RRI = 1.0 / (MAX_HR / 60.0) * 1000
MAX_RRI = 1.0 / (MIN_HR / 60.0) * 1000
train_input_array = []
train_label_array = []
for data_index in range(len(train_data_name)):
print(train_data_name[data_index])
win_num = len(
wfdb.rdann(os.path.join(data_path, train_data_name[data_index]),
'apn').symbol)
signals, fields = wfdb.rdsamp(
os.path.join(data_path, train_data_name[data_index]))
for index in tqdm(range(1, win_num)):
samp_from = index * 60 * FS # 60 seconds
samp_to = samp_from + 60 * FS # 60 seconds
qrs_ann = wfdb.rdann(data_path + train_data_name[data_index],
'qrs',
sampfrom=samp_from - (MARGIN * 100),
sampto=samp_to + (MARGIN * 100)).sample
apn_ann = wfdb.rdann(data_path + train_data_name[data_index],
'apn',
sampfrom=samp_from,
sampto=samp_to - 1).symbol
qrs_amp = get_qrs_amp(signals, qrs_ann)
rri = np.diff(qrs_ann)
rri_ms = rri.astype('float') / FS * 1000.0
try:
rri_filt = moving_median(rri_ms)
if len(rri_filt) > 5 and (np.min(rri_filt) >= MIN_RRI
and np.max(rri_filt) <= MAX_RRI):
time_intp, rri_intp = interp_cubic_spline(
rri_filt, FS_INTP)
qrs_time_intp, qrs_intp = interp_cubic_spline_qrs(
qrs_ann, qrs_amp, FS_INTP)
rri_intp = rri_intp[(time_intp >= MARGIN)
& (time_intp < (60 + MARGIN))]
qrs_intp = qrs_intp[(qrs_time_intp >= MARGIN)
& (qrs_time_intp < (60 + MARGIN))]
#time_intp = time_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
if len(rri_intp) != (FS_INTP * 60):
skip = 1
else:
skip = 0
if skip == 0:
rri_intp = rri_intp - np.mean(rri_intp)
qrs_intp = qrs_intp - np.mean(qrs_intp)
if apn_ann[0] == 'N': # Normal
label = 0.0
elif apn_ann[0] == 'A': # Apnea
label = 1.0
else:
label = 2.0
train_input_array.append([
rri_intp, qrs_intp, age[data_index],
sex[data_index]
])
train_label_array.append(label)
except:
hrv_module_error = 1
print(train_input_array[0])
print(train_label_array[0])
np.save('train_input.npy', train_input_array)
np.save('train_label.npy', train_label_array)
test_input_array = []
test_label_array = []
for data_index in range(len(test_data_name)):
print(test_data_name[data_index])
win_num = len(
wfdb.rdann(os.path.join(data_path, test_data_name[data_index]),
'apn').symbol)
signals, fields = wfdb.rdsamp(
os.path.join(data_path, test_data_name[data_index]))
for index in tqdm(range(1, win_num)):
samp_from = index * 60 * FS # 60 seconds
samp_to = samp_from + 60 * FS # 60 seconds
qrs_ann = wfdb.rdann(data_path + test_data_name[data_index],
'qrs',
sampfrom=samp_from - (MARGIN * 100),
sampto=samp_to + (MARGIN * 100)).sample
apn_ann = wfdb.rdann(data_path + test_data_name[data_index],
'apn',
sampfrom=samp_from,
sampto=samp_to - 1).symbol
qrs_amp = get_qrs_amp(signals, qrs_ann)
rri = np.diff(qrs_ann)
rri_ms = rri.astype('float') / FS * 1000.0
try:
rri_filt = moving_median(rri_ms)
if len(rri_filt) > 5 and (np.min(rri_filt) >= MIN_RRI
and np.max(rri_filt) <= MAX_RRI):
time_intp, rri_intp = interp_cubic_spline(
rri_filt, FS_INTP)
qrs_time_intp, qrs_intp = interp_cubic_spline_qrs(
qrs_ann, qrs_amp, FS_INTP)
rri_intp = rri_intp[(time_intp >= MARGIN)
& (time_intp < (60 + MARGIN))]
qrs_intp = qrs_intp[(qrs_time_intp >= MARGIN)
& (qrs_time_intp < (60 + MARGIN))]
#time_intp = time_intp[(time_intp >= MARGIN) & (time_intp < (60+MARGIN))]
if len(rri_intp) != (FS_INTP * 60):
skip = 1
else:
skip = 0
if skip == 0:
rri_intp = rri_intp - np.mean(rri_intp)
qrs_intp = qrs_intp - np.mean(qrs_intp)
if apn_ann[0] == 'N': # Normal
label = 0.0
elif apn_ann[0] == 'A': # Apnea
label = 1.0
else:
label = 2.0
test_input_array.append([
rri_intp, qrs_intp, age[data_index],
sex[data_index]
])
test_label_array.append(label)
except:
hrv_module_error = 1
np.save('test_input.npy', test_input_array)
np.save('test_label.npy', test_label_array)