# mne.preprocessing.create_ecg_epochs¶

mne.preprocessing.create_ecg_epochs(raw, ch_name=None, event_id=999, picks=None, tmin=- 0.5, tmax=0.5, l_freq=8, h_freq=16, reject=None, flat=None, baseline=None, preload=True, keep_ecg=False, reject_by_annotation=True, decim=1, verbose=None)[source]

Conveniently generate epochs around ECG artifact events.

This function will:

1. Filter the ECG data channel.

2. Find ECG R wave peaks using mne.preprocessing.find_ecg_events().

3. Filter the raw data.

4. Create Epochs around the R wave peaks, capturing the heartbeats.

Note

Filtering is only applied to the ECG channel while finding events. The resulting ecg_epochs will have no filtering applied (i.e., have the same filter properties as the input raw instance).

Parameters
rawinstance of Raw

The raw data.

ch_name

The name of the channel to use for ECG peak detection. If None (default), ECG channel is used if present. If None and no ECG channel is present, a synthetic ECG channel is created from the cross-channel average. This synthetic channel can only be created from MEG channels.

event_idint

The index to assign to found ECG events.

picks

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

tminfloat

Start time before event.

tmaxfloat

End time after event.

l_freqfloat

Low pass frequency to apply to the ECG channel while finding events.

h_freqfloat

High pass frequency to apply to the ECG channel while finding events.

reject

Reject epochs based on peak-to-peak signal amplitude (PTP), i.e. the absolute difference between the lowest and the highest signal value. In each individual epoch, the PTP is calculated for every channel. If the PTP of any one channel exceeds the rejection threshold, the respective epoch will be dropped.

The dictionary keys correspond to the different channel types; valid keys are: 'grad', 'mag', 'eeg', 'eog', and 'ecg'.

Example:

reject = dict(grad=4000e-13,  # unit: T / m (gradiometers)
mag=4e-12,      # unit: T (magnetometers)
eeg=40e-6,      # unit: V (EEG channels)
eog=250e-6      # unit: V (EOG channels)
)


Note

Since rejection is based on a signal difference calculated for each channel separately, applying baseline correction does not affect the rejection procedure, as the difference will be preserved.

If reject is None (default), no rejection is performed.

flat

Rejection parameters based on flatness of signal. Valid keys are 'grad', 'mag', 'eeg', 'eog', 'ecg'. The values are floats that set the minimum acceptable peak-to-peak amplitude (PTP). If the PTP is smaller than this threshold, the epoch will be dropped. If None then no rejection is performed based on flatness of the signal.

baselineNone | tuple of length 2

The time interval to consider as “baseline” when applying baseline correction. If None, do not apply baseline correction. If a tuple (a, b), the interval is between a and b (in seconds), including the endpoints. If a is None, the beginning of the data is used; and if b is None, it is set to the end of the interval. If (None, None), the entire time interval is used.

Note

The baseline (a, b) includes both endpoints, i.e. all timepoints t such that a <= t <= b.

Correction is applied to each epoch and channel individually in the following way:

1. Calculate the mean signal of the baseline period.

2. Subtract this mean from the entire epoch.

Preload epochs or not (default True). Must be True if keep_ecg is True.

keep_ecgbool

When ECG is synthetically created (after picking), should it be added to the epochs? Must be False when synthetic channel is not used. Defaults to False.

reject_by_annotationbool

Whether to reject based on annotations. If True (default), epochs overlapping with segments whose description begins with 'bad' are rejected. If False, no rejection based on annotations is performed.

New in version 0.14.0.

decimint

Factor by which to subsample the data.

Warning

Low-pass filtering is not performed, this simply selects every Nth sample (where N is the value passed to decim), i.e., it compresses the signal (see Notes). If the data are not properly filtered, aliasing artifacts may occur.

New in version 0.21.0.

verbose

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns
ecg_epochsinstance of Epochs

Data epoched around ECG R wave peaks.