def hrv_feature_extraction(inputs, fs=512):
"""
:param inputs: R-R intervals as numpy array
:param fs: ECG sample freq for regularization
:return: list contains 9 features, listed as follow.
"""
# features = {"HR": float,
# "SDNN": float,
# "RMSSD": float,
# "SDSD": float,
# "NN50": float,
# "PNN50": float,
# "LF": float,
# "HF": float,
# "LFHF": float}
features = []
if inputs is []:
return features
else:
# regularization to ms
inputs = np.true_divide(inputs, fs)
inputs = np.multiply(inputs, 1000)
tmp = get_time_domain_features(inputs)
tmp2 = get_frequency_domain_features(inputs, method='lomb')
features.append(tmp['mean_hr'])
features.append(tmp['sdnn'])
features.append(tmp['rmssd'])
features.append(tmp['sdsd'])
features.append(tmp['nni_50'])
features.append(tmp['pnni_50']) # in percentage
features.append(tmp2['lf'])
features.append(tmp2['hf'])
features.append(tmp2['lf_hf_ratio'])
return features
def test_if_time_domain_features_are_correct_for_pnni_as_percent_set_to_false(
self):
nn_intervals = load_test_data(TEST_DATA_FILENAME)
function_time_domain_features = get_time_domain_features(
nn_intervals=nn_intervals, pnni_as_percent=False)
real_function_time_domain_features = {
'mean_nni': 718.248,
'sdnn': 43.113074968427306,
'sdsd': 19.519367520775713,
'nni_50': 24,
'pnni_50': 2.4,
'nni_20': 225,
'pnni_20': 22.5,
'rmssd': 19.519400785039664,
'median_nni': 722.5,
'range_nni': 249,
'cvsd': 0.027176408127888504,
'cvnni': 0.060025332431732914,
'mean_hr': 83.84733227281252,
'max_hr': 101.69491525423729,
'min_hr': 71.51370679380214,
'std_hr': 5.196775370674054
}
self.assertAlmostEqual(function_time_domain_features,
real_function_time_domain_features)
time_series_lf_hf_ratio.append(0)
time_series_sd1.append(0)
time_series_sample_entropy.append(0)
else:
temp_RR = temp[:(i + 1)]
#print(temp_RR)
# Now find the features and extract the ones we require
# For time domain we will use Mean,SDNN,RMSSD,SDSD, pNN20, pNN50
# Frequency domain we will use totalPSD, HF
# Non linear features we will use SD1/SD2 and sample entropy
time_domain = get_time_domain_features(temp_RR)
# Time domain components
time_series_mean_nni.append(time_domain['mean_nni'])
time_series_sdnn.append(time_domain['sdnn'])
time_series_sdsd.append(time_domain['sdsd'])
time_series_pnni_20.append(time_domain['pnni_20'])
time_series_pnni_50.append(time_domain['pnni_50'])
time_series_range_nni.append(time_domain['range_nni'])
frequency_domain = get_frequency_domain_features(
temp_RR, 'welch', 360)
# Frequency domain components
time_series_total_power.append(frequency_domain['total_power'])
time_series_hf.append(frequency_domain['hf'])