def setup_load_ecg_data():
"""Load ecg signal and sampling rate."""
def load_signal_from_disk(filename=None, sampling_rate=2000):
if filename is None:
ecg = nk.ecg_simulate(duration=10,
sampling_rate=sampling_rate,
method="ecgsyn")
else:
filename = (pathlib.Path(__file__) / '../ecg_data' /
filename).resolve().as_posix()
ecg = np.array(pd.read_csv(filename))[:, 1]
return ecg, sampling_rate
ecg, sampling_rate = load_signal_from_disk('good_4000.csv',
sampling_rate=4000)
annots_filename = (pathlib.Path(__file__) / '../ecg_data' /
'good_4000_annotation.csv').resolve().as_posix()
annots = pd.read_csv(annots_filename, index_col=0, header=None).transpose()
if SHOW_DEBUG_PLOTS:
plt.plot(ecg)
plt.show()
rpeaks = nk.ecg_findpeaks(ecg,
sampling_rate=sampling_rate,
method='martinez')['ECG_R_Peaks']
test_data = dict(ecg=ecg, sampling_rate=sampling_rate, rpeaks=rpeaks)
test_data.update(annots)
yield test_data
def test_ecg_fixpeaks():
sampling_rate = 1000
noise = 0.15
ecg = nk.ecg_simulate(duration=120, sampling_rate=sampling_rate,
noise=noise, method="simple", random_state=42)
rpeaks = nk.ecg_findpeaks(ecg)
# Test with iterative artifact correction.
artifacts, rpeaks_corrected = nk.ecg_fixpeaks(rpeaks, iterative=True)
assert np.allclose(rpeaks_corrected["ECG_R_Peaks"].sum(dtype=np.int64),
8624462, atol=1)
assert all(isinstance(x, int) for x in artifacts["ectopic"])
assert all(isinstance(x, int) for x in artifacts["missed"])
assert all(isinstance(x, int) for x in artifacts["extra"])
assert all(isinstance(x, int) for x in artifacts["longshort"])
# Test with non-iterative artifact correction.
artifacts, rpeaks_corrected = nk.ecg_fixpeaks(rpeaks, iterative=False)
assert np.allclose(rpeaks_corrected["ECG_R_Peaks"].sum(dtype=np.int64),
8622997, atol=1)
assert all(isinstance(x, int) for x in artifacts["ectopic"])
assert all(isinstance(x, int) for x in artifacts["missed"])
assert all(isinstance(x, int) for x in artifacts["extra"])
assert all(isinstance(x, int) for x in artifacts["longshort"])
def find_peaks(signal,
fs=125,
show_plot=True,
peak_method="pantompkins1985",
clean_method="neurokit"):
peaks = nk.ecg_findpeaks(ecg_cleaned=signal,
sampling_rate=fs,
show=False,
method=peak_method)
peaks_clean = nk.signal_fixpeaks(
peaks,
sampling_rate=fs,
iterative=True,
show=False,
interval_min=int(fs * .66), # HR 240bpm
interval_max=int(fs * 2), # HR 30bpm
robust=False,
method=clean_method)
# peaks_clean = [i for i in peaks_clean if abs(i) >= 5]
if show_plot:
plt.plot(signal, color='black', zorder=0)
plt.scatter(peaks["ECG_R_Peaks"],
[signal[i] for i in peaks["ECG_R_Peaks"]],
color='red',
marker='x',
zorder=1)
plt.scatter(peaks_clean, [signal[i] for i in peaks_clean],
color='limegreen',
zorder=2)
return peaks, peaks_clean
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)
