API reference#

Automatic sleep staging & events detection#

`SleepStaging`	Automatic sleep staging of polysomnography data.
`art_detect`	Automatic artifact rejection.
`rem_detect`	Rapid eye movements (REMs) detection.
`REMResults`	Output class for REMs detection.
`spindles_detect`	Spindles detection.
`SpindlesResults`	Output class for spindles detection.
`sw_detect`	Slow-waves detection.
`SWResults`	Output class for slow-waves detection.
`compare_detection`	Determine correctness of detected events against ground-truth events.

`Hypnogram`	Standard class for representing and analyzing a sleep hypnogram.
`EpochByEpochAgreement`	Evaluate agreement between two hypnograms or two collections of hypnograms.
`SleepStatsAgreement`	Evaluate agreement between sleep statistics reported by two different scorers.
`simulate_hypnogram`	Simulate a hypnogram based on transition probabilities.

`bandpower`	Calculate the Welch bandpower for each channel and, if specified, for each sleep stage.
`bandpower_from_psd`	Compute the average power of the EEG in specified frequency band(s) given a pre-computed PSD.
`bandpower_from_psd_ndarray`	Compute bandpowers in N-dimensional PSD.
`irasa`	Separate the aperiodic (= fractal, or 1/f) and oscillatory component of the power spectra of EEG data using the IRASA method.
`moving_transform`	Moving transformation of one or two time-series.
`plot_spectrogram`	Plot a full-night multi-taper spectrogram, optionally with the hypnogram on top.
`sliding_window`	Calculate a sliding window of a 1D or 2D EEG signal.
`stft_power`	Compute the pointwise power via STFT and interpolation.
`topoplot`	Topoplot.

Calculate heart rate and heart rate variability (HRV) features from an ECG.

Download (i.e., fetch) a single file, if not already downloaded, from the YASA samples dataset on Zenodo.