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_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_SD2SD1', 'HRV_CSI', 'HRV_CVI',
'HRV_CSI_Modified', 'HRV_SampEn'
]
# Test with signal dataframe
features_df = nk.ecg_intervalrelated(df)
assert all(elem in columns
for elem in np.array(features_df.columns.values, dtype=str))
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
def preprocessing_and_feature_extraction_ECG(raw):#(file_name_csv,raw):
data_ECG={}
for participant in range(0,23):
for video in range(0,18):
# load raw baseline and stimuli data for left and right
basl_l=raw['DREAMER'][0,0]['Data'][0,participant]['ECG'][0,0]['baseline'][0,0][video,0][:,0]
stim_l=raw['DREAMER'][0,0]['Data'][0,participant]['ECG'][0,0]['stimuli'][0,0][video,0][:,0]
basl_r=raw['DREAMER'][0,0]['Data'][0,participant]['ECG'][0,0]['baseline'][0,0][video,0][:,1]
stim_r=raw['DREAMER'][0,0]['Data'][0,participant]['ECG'][0,0]['stimuli'][0,0][video,0][:,1]
# process with neurokit
ecg_signals_b_l,info_b_l=nk.ecg_process(basl_l,sampling_rate=256)
ecg_signals_s_l,info_s_l=nk.ecg_process(stim_l,sampling_rate=256)
ecg_signals_b_r,info_b_r=nk.ecg_process(basl_r,sampling_rate=256)
ecg_signals_s_r,info_s_r=nk.ecg_process(stim_r,sampling_rate=256)
# divide stimuli features by baseline features
# would be interesting to compare classification accuracy when we
# don't do this
features_ecg_l=nk.ecg_intervalrelated(ecg_signals_s_l)/nk.ecg_intervalrelated(ecg_signals_b_l)
features_ecg_r=nk.ecg_intervalrelated(ecg_signals_s_r)/nk.ecg_intervalrelated(ecg_signals_b_r)
# average left and right features
# would be interesting to compare classification accuracy when we
# rather include both left and right features
features_ecg=(features_ecg_l+features_ecg_r)/2
if not len(data_ECG):
data_ECG=features_ecg
else:
data_ECG=pd.concat([data_ECG,features_ecg],ignore_index=True)
return data_ECG
def nothing():
# Quality check algorithms
# qcqrs = run_qc(signal=data.filtered, epoch_len=15, fs=fs, algorithm="averageQRS", show_plot=False)
# Removing invalid data based on QC thresholdling
#t = threshold_averageqrs_data(signal=data.filtered, qc_signal=qc, epoch_len=10, fs=fs, pad_val=0,
# thresh=.95, method='exclusive', plot_data=False)
p, pc = find_peaks(signal=data.filtered,
fs=fs,
show_plot=True,
peak_method="pantompkins1985",
clean_method='neurokit')
hrv = nk.hrv_time(peaks=pc, sampling_rate=data.sample_rate, show=False)
freq = nk.hrv_frequency(peaks=pc,
sampling_rate=data.sample_rate,
show=True)
# df_events, info = test_ecg_process_func(signal=data.filtered[15000:15000+1250], start=0, n_samples=int(10*125), fs=fs, plot_builtin=True, plot_events=False)
# heartbeats = segment_ecg(signal=d, fs=fs)
waves, sigs = nk.ecg_delineate(ecg_cleaned=data.filtered,
rpeaks=pc,
sampling_rate=data.sample_rate,
method='dwt',
show=False)
intervals, ecg_rate, ecg_hrv = nk.ecg_intervalrelated()
# TODO
# Organizing
# Signal quality in organized section --> able to pick which algorithm
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
0, 0]['baseline'][0, 0][j, 0][:, 1]
stim_r = data['DREAMER'][0, 0]['Data'][0, k]['ECG'][
0, 0]['stimuli'][0, 0][j, 0][:, 1]
ecg_signals_b_l, info_b_l = nk.ecg_process(basl_l,
sampling_rate=256)
ecg_signals_s_l, info_s_l = nk.ecg_process(stim_l,
sampling_rate=256)
ecg_signals_b_r, info_b_r = nk.ecg_process(basl_r,
sampling_rate=256)
ecg_signals_s_r, info_s_r = nk.ecg_process(stim_r,
sampling_rate=256)
# processed_ecg_b_l = nk.ecg_intervalrelated(ecg_signals_b_l)
# processed_ecg_s_l = nk.ecg_intervalrelated(ecg_signals_s_l)
# processed_ecg_b_r = nk.ecg_intervalrelated(ecg_signals_b_r)
# processed_ecg_s_r = nk.ecg_intervalrelated(ecg_signals_s_r)
processed_ecg_l = nk.ecg_intervalrelated(
ecg_signals_s_l) / nk.ecg_intervalrelated(ecg_signals_b_l)
processed_ecg_r = nk.ecg_intervalrelated(
ecg_signals_s_r) / nk.ecg_intervalrelated(ecg_signals_b_r)
processed_ecg = (processed_ecg_l + processed_ecg_r) / 2
if not len(ECG):
ECG = processed_ecg
else:
ECG = pd.concat([ECG, processed_ecg], ignore_index=True)
total += 1
print("\rprogress: %d%%" % (total / (23 * 18) * 100), end="")
# col=ECG.columns.values
# scaler=pre.StandardScaler()
# for i in range(len(col)):
# ECG[col[i][:-3]] = scaler.fit_transform(ECG[[col[i]]])
# ECG.drop(col, axis=1, inplace=True)
# ECG.columns=col