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.

`ppg_peaks`(signal, sfreq[, new_sfreq, ...])	Systolic peak detection for PPG signals.
`ecg_peaks`(signal[, sfreq, new_sfreq, ...])	Detect R peaks from ECG recording.
`rsp_peaks`(signal, sfreq[, new_sfreq, ...])	Identify peaks and/or onsets in respiratory signal.
`rr_artefacts`(rr[, c1, c2, alpha, input_type])	Detect artefacts in RR time series.
`interpolate_clipping`(signal[, ...])	Interoplate clipping artefacts.

`nnX`(rr[, t, input_type])	Compute the number of difference in successive R-R interval > t ms.
`pnnX`(rr[, t, input_type])	Compute the 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.
`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 time domain, frequency domain and linear HRV metrics.

`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.

`Oximeter`(serial[, sfreq, add_channels, ...])	Recording PPG signal with Nonin pulse oximeter.
`BrainVisionExG`(ip, sfreq[, port])	Recording ECG signal through TCPIP.
`Nonin3231USB`(serial[, add_channels])	Recording Nonin 3231 USB HR signal through USB connection.

`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.

`Viewer`([bids_folder, preprocessed_folder, ...])	Handles the interaction with BIDS structured folders.
`Editor`(signal, sfreq, signal_type[, ...])	Visualization and manual edition of peaks vectors.

`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, output_unit, kind, ...])	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 intervals after dropping bad segments.
`norm_bad_segments`(bad_segments)	Normalize bad segments.