Functions

Correction

correct_extra_rr(rr, extra_idx[, artefacts])	Correct extra heartbeat(s) by removing the RR interval(s).
correct_missed_rr(rr, missed_idx[, artefacts])	Correct missed heartbeat(s) by adding new RR intervals.
interpolate_rr(rr, idx)	Correct long or short beat(s) using linear interpolation.
correct_rr(rr[, extra_correction, ...])	Correct artefacts in RR time series using the method described in [R5f0966f8069a-1].
correct_peaks(peaks[, input_type, ...])	Correct long, short, extra, missed and ectopic beats in peaks vector.
correct_missed_peaks(peaks, idx)	Correct missed peaks in boolean peak vector.
correct_ectopic_peaks(peaks, idx[, signal])	Correct pseudo ectopic heartbeat in boolean peak vector.

Detection

ppg_peaks(signal, sfreq[, new_sfreq, ...])	Systolic peak detection for PPG signals.
ecg_peaks(signal[, sfreq, new_sfreq, ...])	A simple wrapper for many popular R peaks detectors algorithms.
rsp_peaks(signal, sfreq[, new_sfreq, ...])	Identify peaks and/or onsets in respiratory signal.
rr_artefacts(rr[, c1, c2, alpha, input_type])	Artefacts detection from RR time series using the subspaces approach proposed by Lipponen & Tarvainen (2019).
interpolate_clipping(signal[, ...])	Interoplate clipping artefacts.

Heart Rate Variability

nnX(rr[, t, input_type])	Number of difference in successive R-R interval > t ms.
pnnX(rr[, t, input_type])	Number of successive differences larger than a value (def = 50ms).
rmssd(rr[, input_type])	Root Mean Square of Successive Differences.
time_domain(rr[, input_type])	Extract all time domain parameters from R-R intervals.
psd(rr[, sfreq, method, input_type])	Extract the frequency domain features of heart rate variability.
frequency_domain(rr[, sfreq, method, ...])	Extract the frequency domain features of heart rate variability.
nonlinear_domain(rr[, input_type])	Extract the non-linear features of heart rate variability.
poincare(rr[, input_type])	Compute SD1 and SD2 from the Poincaré nonlinear method for heart rate variability.
recurrence(rr[, input_type])	Compute quantitative metrics from the recurrence plot for heart rate variability.
recurrence_matrix(rr[, m, tau])	Compute the recurrence matrix from an array of RR intervals [R3bbf2e959ea6-1].
all_domain(rr[, input_type])	Extract all the HRV indices implemented for the time domain, frequency domain and linear domain.

Plots

plot_circular(data[, y, hue, labels, units, ...])	Plot polar histogram.
plot_ectopic()	Visualization of ectopic beats detection.
plot_events([triggers, triggers_idx, ...])	Visualize the occurence of events along the physiological recording.
plot_evoked([epochs, signal, triggers, ...])	Plot evoked heart rate across trials.
plot_frequency(rr[, input_type, fbands, ...])	Plot power spectral densty of RR time series.
plot_poincare(rr[, input_type, figsize, ...])	Poincare plot.
plot_raw(signal[, peaks, sfreq, modality, ...])	Visualization of PPG or ECG signal with systolic peaks or R wave detection.
plot_rr(rr[, unit, kind, line, points, ...])	Plot instantaneous heart rate time series.
plot_shortlong()	Visualization of short, long, extra and missed intervals detection.
plot_subspaces([rr, artefacts, input_type, ...])	Visualization of short, long, extra, missed and ectopic beats detection.

Recording

Oximeter(serial[, sfreq, add_channels, ...])	Recording PPG signal with Nonin pulse oximeter.
BrainVisionExG(ip, sfreq[, port])	Recording ECG signal through TCPIP.

Reports

time_table([rr, time_df, input_type, ...])	Format time domain results for nice rendering.
frequency_table([rr, frequency_df, ...])	Format frequency domain results for nice rendering.
nonlinear_table([rr, nonlinear_df, ...])	Format nonlinear domain results for nice rendering.

Interactive data labelling and peaks correction

Viewer([bids_folder, preprocessed_folder, ...])	This class handles the interaction with BIDS structured folders.
Editor(signal, sfreq, signal_type[, ...])	This class handle the visualization and manual edition of peaks vectors associated with physiological signals.

Utils

norm_triggers(triggers[, threshold, n, ...])	Ceaning noisy triggers into boolean vecor with a unique spike for each event.
time_shift(x, events[, order])	Return the delay between x and events.
heart_rate(x[, sfreq, unit, kind, input_type])	Transform peaks data or RR intervals into continuous heart rate time series.
to_angles(x, events)	Angular values of events according to x cycle peaks.
to_epochs(signal[, triggers, triggers_idx, ...])	Epoch signal based on event triggers.
simulate_rr([n_rr, extra_idx, missed_idx, ...])	RR time series simulation with artefacts.
to_neighbour(signal, peaks[, kind, size])	Replace peaks with max/min neighbour in a given window.
input_conversion(x, input_type, output_type)	Convert input time series to the desired output format.
nan_cleaning(signal[, verbose])	Interpolate NaNs values.
find_clipping(signal)	Automatically find the max and/or min threshold value of clipping artefacts.
get_valid_segments(signal[, bad_segments])	Return the longest signal or intervals time series after dropping segments marked as bads.
norm_bad_segments(bad_segments)	Normalize bad segments.