def test_1_successive_outlier_karlsson(self):
rri_list = [110, 100, 125, 100, 100, 70, 100, 130, 105, 100, 78, 100]
self.assertEqual(
remove_ectopic_beats(rr_intervals=rri_list, method="karlsson"), [
110, 100, np.nan, 100, 100, np.nan, 100, np.nan, 105, 100,
np.nan, 100
])
def test_1_successive_outlier_Acer(self):
rri_list = [
100, 100, 100, 100, 100, 100, 100, 100, 110, 930, 110, 100, 10
]
self.assertEqual(
(remove_ectopic_beats(rr_intervals=rri_list, method="acar")), [
100, 100, 100, 100, 100, 100, 100, 100, 110, np.nan, 110, 100,
np.nan
])
def get_peak_error_features(data_sample,
sample_rate=100,
rpeak_detector=0,
low_rri=300,
high_rri=2000):
rules = ["malik", "karlsson", "kamath", "acar"]
try:
wd, m = hp.process(data_sample, sample_rate, calc_freq=True)
except:
try:
wd, m = hp.process(data_sample, sample_rate)
except:
error_dict = {rule + "_error": np.nan for rule in rules}
error_dict["outlier_error"] = np.nan
return error_dict
if rpeak_detector in [1, 2, 3, 4]:
detector = PeakDetector(wave_type='ecg')
peak_list = detector.ppg_detector(data_sample,
rpeak_detector,
preprocess=False)[0]
wd["peaklist"] = peak_list
wd = calc_rr(peak_list, sample_rate, working_data=wd)
wd = check_peaks(wd['RR_list'],
wd['peaklist'],
wd['ybeat'],
reject_segmentwise=False,
working_data=wd)
wd = clean_rr_intervals(working_data=wd)
rr_intervals = wd["RR_list"]
rr_intervals_cleaned = remove_outliers(rr_intervals,
low_rri=low_rri,
high_rri=high_rri)
number_outliers = len(np.where(np.isnan(rr_intervals_cleaned))[0])
outlier_ratio = number_outliers / (len(rr_intervals_cleaned) -
number_outliers)
error_sqi = {}
error_sqi['outlier_error'] = outlier_ratio
interpolated_rr_intervals = interpolate_nan_values(rr_intervals_cleaned)
for rule in rules:
nn_intervals = remove_ectopic_beats(interpolated_rr_intervals,
method=rule)
number_ectopics = len(np.where(np.isnan(nn_intervals))[0])
ectopic_ratio = number_ectopics / (len(nn_intervals) - number_ectopics)
error_sqi[rule + "_error"] = ectopic_ratio
return error_sqi
def ectopic_sqi(
data_sample,
rule_index=0,
sample_rate=100,
rpeak_detector=0,
wave_type='ppg',
low_rri=300,
high_rri=2000,
):
"""
Evaluate the invalid peaks (which exceeds normal range)
base on HRV rules: Malik, Karlsson, Kamath, Acar
Output the ratio of invalid
Parameters
----------
data_sample :
rule_index:
0: Default Outlier Peak
1: Malik
2: Karlsson
3: Kamath
4: Acar
(Default rule is Malik)
sample_rate :
(Default value = 100)
rpeak_detector :
(Default value = 0)
To explain other detector options
low_rri :
(Default value = 300)
high_rri :
(Default value = 2000)
Returns
-------
"""
rules = ["malik", "karlsson", "kamath", "acar"]
try:
wd, m = hp.process(data_sample, sample_rate, calc_freq=True)
except:
try:
wd, m = hp.process(data_sample, sample_rate)
except:
error_dict = {rule + "_error": np.nan for rule in rules}
error_dict["outlier_error"] = np.nan
return error_dict
# if rpeak_detector in [1, 2, 3, 4]:
if wave_type == 'ecg':
detector = PeakDetector(wave_type='ecg')
peak_list = detector.ecg_detector(data_sample, rpeak_detector)[0]
else:
detector = PeakDetector(wave_type='ppg')
peak_list = detector.ppg_detector(data_sample,
rpeak_detector,
preprocess=False)[0]
wd["peaklist"] = peak_list
wd = calc_rr(peak_list, sample_rate, working_data=wd)
wd = check_peaks(wd['RR_list'],
wd['peaklist'],
wd['ybeat'],
reject_segmentwise=False,
working_data=wd)
wd = clean_rr_intervals(working_data=wd)
rr_intervals = wd["RR_list"]
rr_intervals_cleaned = remove_outliers(rr_intervals,
low_rri=low_rri,
high_rri=high_rri)
number_outliers = len(np.where(np.isnan(rr_intervals_cleaned))[0])
outlier_ratio = number_outliers / (len(rr_intervals_cleaned) -
number_outliers)
if rule_index == 0:
return outlier_ratio
# error_sqi = {}
# error_sqi['outlier_error'] = outlier_ratio
interpolated_rr_intervals = interpolate_nan_values(rr_intervals_cleaned)
rule = rules[rule_index]
nn_intervals = remove_ectopic_beats(interpolated_rr_intervals, method=rule)
number_ectopics = len(np.where(np.isnan(nn_intervals))[0])
ectopic_ratio = number_ectopics / (len(nn_intervals) - number_ectopics)
return ectopic_ratio
def test_1_successive_outlier_kamath(self):
rri_list = [101, 110, 100, 140, 100, 100, 70, 100, 130, 115, 100, 78]
self.assertEqual(
remove_ectopic_beats(rr_intervals=rri_list, method="kamath"),
[101, 110, 100, np.nan, 100, 100, np.nan, 100, 130, 115, 100, 78])
def test_1_successive_outlier_malik(self):
rri_list = [100, 110, 100, 130, 100, 100, 70, 100, 120, 100]
self.assertEqual(
remove_ectopic_beats(rr_intervals=rri_list, method="malik"),
[100, 110, 100, np.nan, 100, 100, np.nan, 100, 120, 100])