The open Sleep-EDF Database Expanded database, refer to doc for more information.

class pyhealth.datasets.SleepEDFDataset(root, dataset_name=None, dev=False, refresh_cache=False, **kwargs)[source]#

Bases: BaseSignalDataset

Base EEG dataset for SleepEDF

Dataset is available at

For the Sleep Cassette Study portion:
  • The 153 SC* files (SC = Sleep Cassette) were obtained in a 1987-1991 study of age effects on sleep in healthy Caucasians aged 25-101, without any sleep-related medication [2]. Two PSGs of about 20 hours each were recorded during two subsequent day-night periods at the subjects homes. Subjects continued their normal activities but wore a modified Walkman-like cassette-tape recorder described in chapter VI.4 (page 92) of Bob’s 1987 thesis [7].

  • Files are named in the form SC4ssNEO-PSG.edf where ss is the subject number, and N is the night. The first nights of subjects 36 and 52, and the second night of subject 13, were lost due to a failing cassette or laserdisk.

  • The EOG and EEG signals were each sampled at 100 Hz. The submental-EMG signal was electronically highpass filtered, rectified and low-pass filtered after which the resulting EMG envelope expressed in uV rms (root-mean-square) was sampled at 1Hz. Oro-nasal airflow, rectal body temperature and the event marker were also sampled at 1Hz.

  • Subjects and recordings are further described in the file headers, the descriptive spreadsheet SC-subjects.xls, and in [2].

For the Sleep Telemetry portoin:
  • The 44 ST* files (ST = Sleep Telemetry) were obtained in a 1994 study of temazepam effects on sleep in 22 Caucasian males and females without other medication. Subjects had mild difficulty falling asleep but were otherwise healthy. The PSGs of about 9 hours were recorded in the hospital during two nights, one of which was after temazepam intake, and the other of which was after placebo intake. Subjects wore a miniature telemetry system with very good signal quality described in [8].

  • Files are named in the form ST7ssNJ0-PSG.edf where ss is the subject number, and N is the night.

  • EOG, EMG and EEG signals were sampled at 100 Hz, and the event marker at 1 Hz. The physical marker dimension ID+M-E relates to the fact that pressing the marker (M) button generated two-second deflections from a baseline value that either identifies the telemetry unit (ID = 1 or 2 if positive) or marks an error (E) in the telemetry link if negative. Subjects and recordings are further described in the file headers, the descriptive spreadsheet ST-subjects.xls, and in [1].

  • dataset_name (Optional[str]) – name of the dataset.

  • root (str) – root directory of the raw data. You can choose to use the path to Cassette portion or the Telemetry portion.

  • dev (bool) – whether to enable dev mode (only use a small subset of the data). Default is False.

  • refresh_cache (bool) – whether to refresh the cache; if true, the dataset will be processed from scratch and the cache will be updated. Default is False.


Optional[str], name of the task (e.g., “sleep staging”). Default is None.


Optional[List[Dict]], a list of samples, each sample is a dict with patient_id, record_id, and other task-specific attributes as key. Default is None.


Optional[Dict[str, List[int]]], a dict mapping patient_id to a list of sample indices. Default is None.


Optional[Dict[str, List[int]]], a dict mapping visit_id to a list of sample indices. Default is None.


>>> from pyhealth.datasets import SleepEDFDataset
>>> dataset = SleepEDFDataset(
...         root="/srv/local/data/SLEEPEDF/sleep-edf-database-expanded-1.0.0/sleep-cassette",
...     )
>>> dataset.stat()