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 test_bio_process():
sampling_rate = 1000
# Create data
ecg = nk.ecg_simulate(duration=30, sampling_rate=sampling_rate)
rsp = nk.rsp_simulate(duration=30, sampling_rate=sampling_rate)
eda = nk.eda_simulate(duration=30,
sampling_rate=sampling_rate,
scr_number=3)
emg = nk.emg_simulate(duration=30,
sampling_rate=sampling_rate,
burst_number=3)
bio_df, bio_info = nk.bio_process(ecg=ecg,
rsp=rsp,
eda=eda,
emg=emg,
sampling_rate=sampling_rate)
# SCR components
scr = [val for key, val in bio_info.items() if "SCR" in key]
assert all(len(elem) == len(scr[0]) for elem in scr)
assert all(bio_info["SCR_Onsets"] < bio_info["SCR_Peaks"])
assert all(bio_info["SCR_Peaks"] < bio_info["SCR_Recovery"])
# RSP
assert all(bio_info["RSP_Peaks"] > bio_info["RSP_Troughs"])
assert len(bio_info["RSP_Peaks"]) == len(bio_info["RSP_Troughs"])
# EMG
assert all(bio_info["EMG_Offsets"] > bio_info["EMG_Onsets"])
assert len(bio_info["EMG_Offsets"] == len(bio_info["EMG_Onsets"]))
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)
def test_bio_process():
sampling_rate = 1000
# Create data
ecg = nk.ecg_simulate(duration=30, sampling_rate=sampling_rate)
rsp = nk.rsp_simulate(duration=30, sampling_rate=sampling_rate)
eda = nk.eda_simulate(duration=30,
sampling_rate=sampling_rate,
scr_number=3)
emg = nk.emg_simulate(duration=30,
sampling_rate=sampling_rate,
burst_number=3)
bio_df, bio_info = nk.bio_process(ecg=ecg,
rsp=rsp,
eda=eda,
emg=emg,
sampling_rate=sampling_rate)
# SCR components
scr = [val for key, val in bio_info.items() if "SCR" in key]
assert all(len(elem) == len(scr[0]) for elem in scr)
assert all(bio_info["SCR_Onsets"] < bio_info["SCR_Peaks"])
assert all(bio_info["SCR_Peaks"] < bio_info["SCR_Recovery"])
# RSP
assert all(bio_info["RSP_Peaks"] > bio_info["RSP_Troughs"])
assert len(bio_info["RSP_Peaks"]) == len(bio_info["RSP_Troughs"])
# EMG
assert all(bio_info["EMG_Offsets"] > bio_info["EMG_Onsets"])
assert len(bio_info["EMG_Offsets"] == len(bio_info["EMG_Onsets"]))
assert all(elem in [
'ECG_Raw', 'ECG_Clean', 'ECG_Rate', 'ECG_Quality', 'ECG_R_Peaks',
"ECG_P_Peaks", "ECG_Q_Peaks", "ECG_S_Peaks", "ECG_T_Peaks",
"ECG_P_Onsets", "ECG_T_Offsets", "ECG_Atrial_Phase",
"ECG_Ventricular_Phase", "ECG_Atrial_PhaseCompletion",
"ECG_Ventricular_PhaseCompletion", 'RSP_Raw', 'RSP_Clean',
'RSP_Amplitude', 'RSP_Rate', 'RSP_Phase', 'RSP_PhaseCompletion',
'RSP_Peaks', 'RSP_Troughs', 'EDA_Raw', 'EDA_Clean', 'EDA_Tonic',
'EDA_Phasic', 'SCR_Onsets', 'SCR_Peaks', 'SCR_Height', 'SCR_Amplitude',
'SCR_RiseTime', 'SCR_Recovery', 'SCR_RecoveryTime', 'EMG_Raw',
'EMG_Clean', 'EMG_Amplitude', 'EMG_Activity', 'EMG_Onsets',
'EMG_Offsets', 'RSA_P2T'
] for elem in np.array(bio_df.columns.values, dtype=str))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import neurokit2 as nk
# =============================================================================
# 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)
for i in range(len(df_gsr.index.values) - 1):
curr_index_value = df_gsr.index.values[i]
curr_column_value = df_gsr[curr_index_value]
next_index_value = df_gsr.index.values[i + 1]
next_column_value = df_gsr[next_index_value]
timedelta_index_value = next_index_value - curr_index_value
insert_index_value = curr_index_value + timedelta_index_value / 2.0
insert_column_value = curr_column_value + (
next_column_value - curr_column_value) / 2.0
df_gsr[insert_index_value] = insert_column_value
df_gsr.sort_index(inplace=True)
freq_gsr = pd.infer_freq(df_gsr.index)
freq_delta_gsr = pd.Timedelta(freq_gsr)
hz_gsr = 1 / freq_delta_gsr.total_seconds()
# Process EDA
eda_signals, eda_info = nk.bio_process(
eda=df_gsr, sampling_rate=hz_gsr)
# analyze event-related features of BVP signals
# eda_analysis = nk.bio_analyze(eda_signals, method="interval-related")
# Feature SCR (Skin Conductance Response)
eda_response_desc = eda_signals.loc[
eda_signals['SCR_Amplitude'] > 0,
["SCR_Amplitude"]].describe()
eda_response_count = eda_response_desc.loc[
'count'].values[0]
row['EDA_Response_Count'] = eda_response_count
eda_response_mean = eda_response_desc.loc[
'mean'].values[0]
row['EDA_Response_Mean'] = eda_response_mean
eda_response_std = eda_response_desc.loc['std'].values[
0]
