def test_bio_analyze():
# Example with event-related analysis
data = nk.data("bio_eventrelated_100hz")
df, info = nk.bio_process(ecg=data["ECG"],
rsp=data["RSP"],
eda=data["EDA"],
keep=data["Photosensor"],
sampling_rate=100)
events = nk.events_find(
data["Photosensor"],
threshold_keep="below",
event_conditions=["Negative", "Neutral", "Neutral", "Negative"])
epochs = nk.epochs_create(df,
events,
sampling_rate=100,
epochs_start=-0.1,
epochs_end=1.9)
event_related = nk.bio_analyze(epochs)
assert len(event_related) == len(epochs)
labels = [int(i) for i in event_related["Label"]]
assert labels == list(np.arange(1, len(epochs) + 1))
# Example with interval-related analysis
data = nk.data("bio_resting_8min_100hz")
df, info = nk.bio_process(ecg=data["ECG"],
rsp=data["RSP"],
eda=data["EDA"],
sampling_rate=100)
interval_related = nk.bio_analyze(df)
assert len(interval_related) == 1
def test_bio_analyze():
# Example with event-related analysis
data = nk.data("bio_eventrelated_100hz")
df, info = nk.bio_process(ecg=data["ECG"],
rsp=data["RSP"],
eda=data["EDA"],
keep=data["Photosensor"],
sampling_rate=100)
events = nk.events_find(
data["Photosensor"],
threshold_keep='below',
event_conditions=["Negative", "Neutral", "Neutral", "Negative"])
epochs = nk.epochs_create(df,
events,
sampling_rate=100,
epochs_start=-0.1,
epochs_end=1.9)
event_related = nk.bio_analyze(epochs)
assert len(event_related) == len(epochs)
labels = [int(i) for i in event_related['Label']]
assert labels == list(np.arange(1, len(epochs) + 1))
assert all(elem in [
'ECG_Rate_Max', 'ECG_Rate_Min', 'ECG_Rate_Mean', 'ECG_Rate_Max_Time',
'ECG_Rate_Min_Time', 'ECG_Rate_Trend_Quadratic',
'ECG_Rate_Trend_Linear', 'ECG_Rate_Trend_R2', 'ECG_Atrial_Phase',
'ECG_Atrial_PhaseCompletion', 'ECG_Ventricular_Phase',
'ECG_Ventricular_PhaseCompletion', 'ECG_Quality_Mean', 'RSP_Rate_Max',
'RSP_Rate_Min', 'RSP_Rate_Mean', 'RSP_Rate_Max_Time',
'RSP_Rate_Min_Time', 'RSP_Amplitude_Max', 'RSP_Amplitude_Min',
'RSP_Amplitude_Mean', 'RSP_Phase', 'RSP_PhaseCompletion', 'EDA_SCR',
'EDA_Peak_Amplitude', 'SCR_Peak_Amplitude', 'SCR_Peak_Amplitude_Time',
'SCR_RiseTime', 'SCR_RecoveryTime', 'RSA_P2T', 'Label', 'Condition'
] for elem in np.array(event_related.columns.values, dtype=str))
# Example with interval-related analysis
data = nk.data("bio_resting_8min_100hz")
df, info = nk.bio_process(ecg=data["ECG"],
rsp=data["RSP"],
eda=data["EDA"],
sampling_rate=100)
interval_related = nk.bio_analyze(df)
assert len(interval_related) == 1
assert all(elem in [
'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', 'RSP_Rate_Mean',
'RSP_Amplitude_Mean', 'RRV_SDBB', 'RRV_RMSSD', 'RRV_SDSD', 'RRV_VLF',
'RRV_LF', 'RRV_HF', 'RRV_LFHF', 'RRV_LFn', 'RRV_HFn', 'RRV_SD1',
'RRV_SD2', 'RRV_SD2SD1', 'RRV_ApEn', 'RRV_SampEn', 'RRV_DFA',
'RSA_P2T_Mean', 'RSA_P2T_Mean_log', 'RSA_P2T_SD', 'RSA_P2T_NoRSA',
'RSA_PorgesBohrer', 'SCR_Peaks_N', 'SCR_Peaks_Amplitude_Mean'
] for elem in np.array(interval_related.columns.values, dtype=str))
def get_12ECG_features(data, header_data):
df = pd.DataFrame(data).T
processed_data2, info2 = nk.bio_process(ecg=df[0], sampling_rate=500)
results2 = nk.bio_analyze(processed_data2, sampling_rate=500)
# results2['label'] = header_data[-4][5:-1]
return results2.replace([pd.np.inf, -pd.np.inf],
pd.np.NaN).fillna(0).values.flatten()
def loadRealdata():
# Download example data
data = nk.data("bio_eventrelated_100hz")
# Preprocess the data (filter, find peaks, etc.)
processed_data, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], sampling_rate=100)
# Compute relevant features
results = nk.bio_analyze(processed_data, sampling_rate=100)
# =============================================================================
# Quick Example
# =============================================================================
# Download example data
data = nk.data("bio_eventrelated_100hz")
# Preprocess the data (filter, find peaks, etc.)
processed_data, info = nk.bio_process(ecg=data["ECG"],
rsp=data["RSP"],
eda=data["EDA"],
sampling_rate=100)
# Compute relevant features
results = nk.bio_analyze(processed_data, sampling_rate=100)
# =============================================================================
# Simulate physiological signals
# =============================================================================
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
ppg = nk.ppg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, scr_number=3)
emg = nk.emg_simulate(duration=10, burst_number=2)
# Visualise biosignals
data = pd.DataFrame({
"ECG": ecg,
ppg_signals, ppg_info = nk.bio_process(
ppg=df_bvp, sampling_rate=BVP_HZ)
# create epochs
ppg_epochs_end = sc_duration if (
sc_duration -
EPOCH_START) <= EPOCH_DURATION else (
EPOCH_START + EPOCH_DURATION)
ppg_epochs = nk.epochs_create(
ppg_signals,
bvp_events,
sampling_rate=BVP_HZ,
epochs_start=EPOCH_START,
epochs_end=ppg_epochs_end)
ppg_epoch = ppg_epochs['1']
# analyze event-related features of EDA signals
ppg_analysis = nk.bio_analyze(
ppg_epochs, method="event-related")
print(ppg_analysis)
exit()
# describe BVP peak results
df_bvp_peak_desc = ppg_epoch.loc[
ppg_epoch['PPG_Peaks'] > 0,
'PPG_Clean'].describe()
bvp_peak_count = df_bvp_peak_desc['count']
row['BVP_Peak_Count'] = bvp_peak_count
bvp_peak_mean = df_bvp_peak_desc['mean']
row['BVP_Peak_Mean'] = bvp_peak_mean
bvp_peak_std = df_bvp_peak_desc['std']
row['BVP_Peak_SD'] = bvp_peak_std
bvp_peak_max = df_bvp_peak_desc['max']
row['BVP_Peak_Max'] = bvp_peak_max
bvp_peak_min = df_bvp_peak_desc['min']
