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tests_ecg.py
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/
tests_ecg.py
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# -*- coding: utf-8 -*-
import biosppy
import matplotlib.pyplot as plt
import numpy as np
import neurokit2 as nk
def test_ecg_simulate():
ecg1 = nk.ecg_simulate(duration=20, length=5000, method="simple", noise=0)
assert len(ecg1) == 5000
ecg2 = nk.ecg_simulate(duration=20, length=5000, heart_rate=500)
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).plot()
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).hist()
assert len(nk.signal_findpeaks(ecg1, height_min=0.6)["Peaks"]) < len(
nk.signal_findpeaks(ecg2, height_min=0.6)["Peaks"]
)
ecg3 = nk.ecg_simulate(duration=10, length=5000)
# pd.DataFrame({"ECG1":ecg1, "ECG3": ecg3}).plot()
assert len(nk.signal_findpeaks(ecg2, height_min=0.6)["Peaks"]) > len(
nk.signal_findpeaks(ecg3, height_min=0.6)["Peaks"]
)
def test_ecg_clean():
sampling_rate = 1000
noise = 0.05
ecg = nk.ecg_simulate(sampling_rate=sampling_rate, noise=noise)
ecg_cleaned_nk = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")
assert ecg.size == ecg_cleaned_nk.size
# Assert that highpass filter with .5 Hz lowcut was applied.
fft_raw = np.abs(np.fft.rfft(ecg))
fft_nk = np.abs(np.fft.rfft(ecg_cleaned_nk))
freqs = np.fft.rfftfreq(ecg.size, 1 / sampling_rate)
assert np.sum(fft_raw[freqs < 0.5]) > np.sum(fft_nk[freqs < 0.5])
# Comparison to biosppy (https://github.com/PIA-Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L69)
ecg_biosppy = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="biosppy")
original, _, _ = biosppy.tools.filter_signal(
signal=ecg,
ftype="FIR",
band="bandpass",
order=int(0.3 * sampling_rate),
frequency=[3, 45],
sampling_rate=sampling_rate,
)
assert np.allclose((ecg_biosppy - original).mean(), 0, atol=1e-6)
def test_ecg_peaks():
sampling_rate = 1000
noise = 0.15
ecg = nk.ecg_simulate(duration=120, sampling_rate=sampling_rate, noise=noise, random_state=42)
ecg_cleaned_nk = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")
# Test without request to correct artifacts.
signals, info = nk.ecg_peaks(ecg_cleaned_nk, correct_artifacts=False, method="neurokit")
assert signals.shape == (120000, 1)
assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64), 139, atol=1)
# Test with request to correct artifacts.
signals, info = nk.ecg_peaks(ecg_cleaned_nk, correct_artifacts=True, method="neurokit")
assert signals.shape == (120000, 1)
assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64), 139, atol=1)
def test_ecg_process():
sampling_rate = 1000
noise = 0.05
ecg = nk.ecg_simulate(sampling_rate=sampling_rate, noise=noise)
signals, info = nk.ecg_process(ecg, sampling_rate=sampling_rate, method="neurokit")
def test_ecg_plot():
ecg = nk.ecg_simulate(duration=60, heart_rate=70, noise=0.05)
ecg_summary, _ = nk.ecg_process(ecg, sampling_rate=1000, method="neurokit")
# Plot data over samples.
nk.ecg_plot(ecg_summary)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 2
titles = ["Raw and Cleaned Signal", "Heart Rate"]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
assert fig.get_axes()[1].get_xlabel() == "Samples"
np.testing.assert_array_equal(fig.axes[0].get_xticks(), fig.axes[1].get_xticks())
plt.close(fig)
# Plot data over seconds.
nk.ecg_plot(ecg_summary, sampling_rate=1000)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 3
titles = ["Raw and Cleaned Signal", "Heart Rate", "Individual Heart Beats"]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
assert fig.get_axes()[1].get_xlabel() == "Time (seconds)"
np.testing.assert_array_equal(fig.axes[0].get_xticks(), fig.axes[1].get_xticks())
plt.close(fig)
def test_ecg_findpeaks():
sampling_rate = 1000
ecg = nk.ecg_simulate(duration=60, sampling_rate=sampling_rate, noise=0, method="simple", random_state=42)
ecg_cleaned = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")
# Test neurokit methodwith show=True
info_nk = nk.ecg_findpeaks(ecg_cleaned, show=True)
assert info_nk["ECG_R_Peaks"].size == 69
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 2
# Test pantompkins1985 method
info_pantom = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="pantompkins1985"), method="pantompkins1985")
assert info_pantom["ECG_R_Peaks"].size == 70
# Test hamilton2002 method
info_hamilton = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="hamilton2002"), method="hamilton2002")
assert info_hamilton["ECG_R_Peaks"].size == 69
# Test christov2004 method
info_christov = nk.ecg_findpeaks(ecg_cleaned, method="christov2004")
assert info_christov["ECG_R_Peaks"].size == 273
# Test gamboa2008 method
info_gamboa = nk.ecg_findpeaks(ecg_cleaned, method="gamboa2008")
assert info_gamboa["ECG_R_Peaks"].size == 69
# Test elgendi2010 method
info_elgendi = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="elgendi2010"), method="elgendi2010")
assert info_elgendi["ECG_R_Peaks"].size == 70
# Test engzeemod2012 method
info_engzeemod = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="engzeemod2012"), method="engzeemod2012")
assert info_engzeemod["ECG_R_Peaks"].size == 70
# Test kalidas2017 method
info_kalidas = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="kalidas2017"), method="kalidas2017")
assert np.allclose(info_kalidas["ECG_R_Peaks"].size, 68, atol=1)
# Test martinez2003 method
ecg = nk.ecg_simulate(duration=60, sampling_rate=sampling_rate, noise=0, random_state=42)
ecg_cleaned = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")
info_martinez = nk.ecg_findpeaks(ecg_cleaned, method="martinez2003")
assert np.allclose(info_martinez["ECG_R_Peaks"].size, 69, atol=1)
def test_ecg_eventrelated():
ecg, info = nk.ecg_process(nk.ecg_simulate(duration=20))
epochs = nk.epochs_create(ecg, events=[5000, 10000, 15000], epochs_start=-0.1, epochs_end=1.9)
ecg_eventrelated = nk.ecg_eventrelated(epochs)
# Test rate features
assert np.alltrue(np.array(ecg_eventrelated["ECG_Rate_Min"]) < np.array(ecg_eventrelated["ECG_Rate_Mean"]))
assert np.alltrue(np.array(ecg_eventrelated["ECG_Rate_Mean"]) < np.array(ecg_eventrelated["ECG_Rate_Max"]))
assert len(ecg_eventrelated["Label"]) == 3
def test_ecg_delineate():
sampling_rate = 1000
# test with simulated signals
ecg = nk.ecg_simulate(duration=20, sampling_rate=sampling_rate, random_state=42)
_, rpeaks = nk.ecg_peaks(ecg, sampling_rate=sampling_rate)
number_rpeaks = len(rpeaks["ECG_R_Peaks"])
# Method 1: derivative
_, waves_derivative = nk.ecg_delineate(ecg, rpeaks, sampling_rate=sampling_rate)
assert len(waves_derivative["ECG_P_Peaks"]) == number_rpeaks
assert len(waves_derivative["ECG_Q_Peaks"]) == number_rpeaks
assert len(waves_derivative["ECG_S_Peaks"]) == number_rpeaks
assert len(waves_derivative["ECG_T_Peaks"]) == number_rpeaks
assert len(waves_derivative["ECG_P_Onsets"]) == number_rpeaks
assert len(waves_derivative["ECG_T_Offsets"]) == number_rpeaks
# Method 2: CWT
_, waves_cwt = nk.ecg_delineate(ecg, rpeaks, sampling_rate=sampling_rate, method="cwt")
assert np.allclose(len(waves_cwt["ECG_P_Peaks"]), 22, atol=1)
assert np.allclose(len(waves_cwt["ECG_T_Peaks"]), 22, atol=1)
assert np.allclose(len(waves_cwt["ECG_R_Onsets"]), 23, atol=1)
assert np.allclose(len(waves_cwt["ECG_R_Offsets"]), 23, atol=1)
assert np.allclose(len(waves_cwt["ECG_P_Onsets"]), 22, atol=1)
assert np.allclose(len(waves_cwt["ECG_P_Offsets"]), 22, atol=1)
assert np.allclose(len(waves_cwt["ECG_T_Onsets"]), 22, atol=1)
assert np.allclose(len(waves_cwt["ECG_T_Offsets"]), 22, atol=1)
def test_ecg_intervalrelated():
data = nk.data("bio_resting_5min_100hz")
df, info = nk.ecg_process(data["ECG"], sampling_rate=100)
columns = ['ECG_Rate_Mean', 'HRV_RMSSD', 'HRV_MeanNN', 'HRV_SDNN', 'HRV_SDSD',
'HRV_CVNN', 'HRV_CVSD', 'HRV_MedianNN', 'HRV_MadNN', 'HRV_MCVNN',
'HRV_IQRNN', 'HRV_pNN50', 'HRV_pNN20', 'HRV_TINN', 'HRV_HTI',
'HRV_ULF', 'HRV_VLF', 'HRV_LF', 'HRV_HF', 'HRV_VHF', 'HRV_LFHF',
'HRV_LFn', 'HRV_HFn', 'HRV_LnHF', 'HRV_SD1', 'HRV_SD2',
'HRV_SD1SD2', 'HRV_S', 'HRV_CSI', 'HRV_CVI', 'HRV_CSI_Modified',
'HRV_PIP', 'HRV_IALS', 'HRV_PSS', 'HRV_PAS', 'HRV_ApEn',
'HRV_SampEn', 'HRV_GI', 'HRV_SI', 'HRV_AI', 'HRV_PI',
'HRV_C1d', 'HRV_C1a', 'HRV_SD1d',
'HRV_SD1a', 'HRV_C2d',
'HRV_C2a', 'HRV_SD2d', 'HRV_SD2a',
'HRV_Cd', 'HRV_Ca', 'HRV_SDNNd',
'HRV_SDNNa']
# Test with signal dataframe
features_df = nk.ecg_intervalrelated(df, sampling_rate=100)
# https://github.com/neuropsychology/NeuroKit/issues/304
assert all(features_df == nk.ecg_analyze(df, sampling_rate=100, method="interval-related"))
assert all(elem in np.array(features_df.columns.values, dtype=str) for elem
in columns)
assert features_df.shape[0] == 1 # Number of rows
# Test with dict
epochs = nk.epochs_create(df, events=[0, 15000],
sampling_rate=100, epochs_end=150)
features_dict = nk.ecg_intervalrelated(epochs, sampling_rate=100)
assert all(elem in columns for elem
in np.array(features_dict.columns.values, dtype=str))
assert features_dict.shape[0] == 2 # Number of rows