Bio-related Functions

Main

bio_process()

bio_process(ecg=None, rsp=None, eda=None, emg=None, ppg=None, eog=None, keep=None, sampling_rate=1000)

Automated processing of bio signals

Wrapper for other bio processing functions of electrocardiography signals (ECG), respiration signals (RSP), electrodermal activity (EDA) and electromyography signals (EMG).

Parameters:

• data (DataFrame) – The DataFrame containing all the respective signals (e.g., ecg, rsp, Photosensor etc.). If provided, there is no need to fill in the other arguments denoting the channel inputs. Defaults to None.

• ecg (Union[list, np.array, pd.Series]) – The raw ECG channel.

• rsp (Union[list, np.array, pd.Series]) – The raw RSP channel (as measured, for instance, by a respiration belt).

• eda (Union[list, np.array, pd.Series]) – The raw EDA channel.

• emg (Union[list, np.array, pd.Series]) – The raw EMG channel.

• ppg (Union[list, np.array, pd.Series]) – The raw PPG channel.

• eog (Union[list, np.array, pd.Series]) – The raw EOG channel, extracted from mne_channel_extract().()

• keep (DataFrame) – Dataframe or channels to add by concatenation to the processed dataframe (for instance, the Photosensor channel).

• sampling_rate (int) – The sampling frequency of the signals (in Hz, i.e., samples/second). Defaults to 1000.

Returns:

• bio_df (DataFrame) – DataFrames of the following processed bio features:

  • ”ECG”: the raw signal, the cleaned signal, the heart rate, and the R peaks indexes. Also generated by ecg_process().

  • ”RSP”: the raw signal, the cleaned signal, the rate, and the amplitude. Also generated by rsp_process().

  • ”EDA”: the raw signal, the cleaned signal, the tonic component, the phasic component, indexes of the SCR onsets, peaks, amplitudes, and half-recovery times. Also generated by eda_process().

  • ”EMG”: the raw signal, the cleaned signal, and amplitudes. Also generated by emg_process().

  • ”PPG”: the raw signal, the cleaned signal, rate and peaks. Also generated by ppg_process().

  • ”RSA”: Respiratory Sinus Arrhythmia features generated by ecg_rsa(), if both ECG and RSP are provided.

  • ”EOG”: the raw signal, the cleaned signal, the indexes of EOG blinks, and the blink rate.

• bio_info (dict) – A dictionary containing the samples of peaks, troughs, amplitudes, onsets, offsets, periods of activation, recovery times of the respective processed signals, as well as the signals’ sampling rate.

See also

ecg_process, rsp_process, eda_process, emg_process, ppg_process, eog_process

Example

Example 1: Using synthetic data

In [1]: import neurokit2 as nk

# With Simulated Data
In [2]: ecg = nk.ecg_simulate(duration=40, sampling_rate=250)

In [3]: rsp = nk.rsp_simulate(duration=40, sampling_rate=250)

In [4]: eda = nk.eda_simulate(duration=40, sampling_rate=250, scr_number=3)

In [5]: emg = nk.emg_simulate(duration=40, sampling_rate=250, burst_number=5)

In [6]: bio_df, bio_info = nk.bio_process(ecg=ecg, rsp=rsp, eda=eda, emg=emg, sampling_rate=250)

In [7]: bio_df.head()
Out[7]: 
    ECG_Raw  ECG_Clean   ECG_Rate  ...  EMG_Offsets  RSA_P2T  RSA_Gates
0  1.099482  -0.075297  69.998963  ...            0      4.0   7.885596
1  1.046464  -0.199778  69.998963  ...            0      4.0   7.885596
2  0.908817  -0.337604  69.998963  ...            0      4.0   7.885596
3  0.712281  -0.493672  69.998963  ...            0      4.0   7.885596
4  0.484394  -0.665154  69.998963  ...            0      4.0   7.885596

[5 rows x 49 columns]

# Visualize a subset of signals
In [8]: bio_df.iloc[:, 0:16].plot(subplots=True)
Out[8]: 
array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,
       <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,
       <Axes: >, <Axes: >, <Axes: >, <Axes: >], dtype=object)

Example 2: Using a real dataset

# Download EOG signal separately
In [9]: eog = nk.data('eog_100hz')

# Download data but crop with same length as eog signal
In [10]: data = nk.data('bio_eventrelated_100hz')[:len(eog)]

In [11]: bio_df, bio_info = nk.bio_process(ecg=data['ECG'], rsp=data['RSP'], eda=data['EDA'],
   ....: emg=None, eog=eog, keep=data['Photosensor'], sampling_rate=100)
   ....: 

# Visualize all signals
In [12]: bio_df.iloc[:, 0:16].plot(subplots=True)
Out[12]: 
array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,
       <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,
       <Axes: >, <Axes: >, <Axes: >, <Axes: >], dtype=object)

bio_analyze()

bio_analyze(data, sampling_rate=1000, method='auto', window_lengths='constant')

Automated analysis of physiological signals

Wrapper for other bio analyze functions of electrocardiography signals (ECG), respiration signals (RSP), electrodermal activity (EDA), electromyography signals (EMG) and electrooculography signals (EOG).

Parameters:

• data (DataFrame) – The DataFrame containing all the processed signals, typically produced by bio_process(), ecg_process(), rsp_process(), eda_process(), emg_process() or eog_process(). Can also be an epochs object.

• sampling_rate (int) – The sampling frequency of the signals (in Hz, i.e., samples/second). Defaults to 1000.

• method (str) – Can be one of "event-related" for event-related analysis on epochs, or "interval-related" for analysis on longer periods of data. Defaults to auto where the right method will be chosen based on the mean duration of the data (event-related for duration under 10s).

• window_lengths (dict) – If constant (default), will use the full epoch for all the signals. Can also be a dictionary with the epoch start and end times for different types of signals, e.g., window_lengths = {"ECG": [0.5, 1.5], "EDA": [0.5, 3.5]}

Returns:

DataFrame – DataFrame of the analyzed bio features. See docstrings of ecg_analyze(), rsp_analyze(), eda_analyze(), emg_analyze() and eog_analyze() for more details. Also returns Respiratory Sinus Arrhythmia features produced by hrv_rsa() if interval-related analysis is carried out.

See also

ecg_analyze, rsp_analyze, eda_analyze, emg_analyze, eog_analyze

Examples

Example 1: Event-related analysis

In [1]: import neurokit2 as nk

# Download data
In [2]: data = nk.data("bio_eventrelated_100hz")

# Process the data
In [3]: df, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"],
   ...:                           keep=data["Photosensor"], sampling_rate=100)
   ...: 

# Build epochs around photosensor-marked events
In [4]: events = nk.events_find(data["Photosensor"], threshold_keep="below",
   ...:                         event_conditions=["Negative", "Neutral",
   ...:                                           "Neutral", "Negative"])
   ...: 

In [5]: epochs = nk.epochs_create(df, events, sampling_rate=100, epochs_start=-0.1,
   ...:                           epochs_end=1.9)
   ...: 

# Analyze
In [6]: nk.bio_analyze(epochs, sampling_rate=100)
Out[6]: 
  Label Condition  Event_Onset  ...  SCR_RecoveryTime    RSA_P2T     RSA_Gates
1     1  Negative         1024  ...               NaN   7.111969  1.776357e-15
2     2   Neutral         4957  ...               NaN -15.476818  1.203761e-02
3     3   Neutral         9224  ...               NaN  -6.685871  2.738234e-02
4     4  Negative        12984  ...               NaN  -0.397892 -5.451553e-03

[4 rows x 46 columns]

Example 2: Interval-related analysis

# Download data
In [7]: data = nk.data("bio_resting_5min_100hz")

# Process the data
In [8]: df, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], ppg=data["PPG"], sampling_rate=100)

# Analyze
In [9]: nk.bio_analyze(df, sampling_rate=100)
Out[9]: 
  ECG_Rate_Mean             HRV_MeanNN  ... RSA_Gates_Mean_log RSA_Gates_SD
0     86.392105  [[694.7563805104409]]  ...           2.045955     0.156187

[1 rows x 232 columns]