The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature. Corresponding hypnograms (sleep patterns) were manually scored by well-trained technicians according to the Rechtschaffen and Kales manual, and are also available.

The CAP Sleep Database is a collection of 108 polysomnographic recordings registered at the Sleep Disorders Center of the Ospedale Maggiore of Parma, Italy. The waveforms (contained in the .edf files of the database) include at least 3 EEG channels (F3 or F4, C3 or C4 and O1 or O2, referred to A1 or A2), EOG (2 channels), EMG of the submentalis muscle, bilateral anterior tibial EMG, respiration signals (airflow, abdominal and thoracic effort and SaO2) and EKG. Additional traces include EEG bipolar traces, according to the 10-20 international system (Fp1-F3, F3-C3, C3-P3, P3-O1 and/or Fp2-F4, F4-C4, C4-P4, P4-O2).

The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~19K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting.

Sleep EEG dataset from 8 subjects in European Data Format (EDF) including original recordings and their hypnograms as described in B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery��. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000). The recordings were obtained from Caucasian males and females (21 - 35 years old) without any medication; they contain horizontal EOG, FpzCz and PzOz EEG, each sampled at 100 Hz. The sc* recordings also contain the submental-EMG envelope, oro-nasal airflow, rectal body temperature and an event marker, all sampled at 1 Hz. The st* recordings contain submental EMG sampled at 100 Hz and an event marker sampled at 1 Hz. The 4 sc* recordings were obtained in 1989 from ambulatory healthy volunteers during 24 hours in their normal daily life, using a modified cassette tape recorder. The 4 st* recordings were obtained in 1994 from subjects who had mild difficulty falling asleep but were otherwise healthy, during a night in the hospital, using a miniature telemetry system with very good signal quality.

Online Sleep Survey dataThese data were obtained over the course of several years. The primary purpose was to build a database of subjects from which I could recruit for my Sleep and Decision Making research studies. Data included are basic demographics some self report sleep data, a validated short form measure of morningness/eveningness preferences, and screener questions for anxiety and depressive disorder (as well as self-reported sleep disorder).

The Sleep Heart Health Study (SHHS) is a multi-center cohort study implemented by the National Heart Lung & Blood Institute to determine the cardiovascular and other consequences of sleep-disordered breathing. It tests whether sleep-related breathing is associated with an increased risk of coronary heart disease, stroke, all cause mortality, and hypertension. In all, 6,441 men and women aged 40 years and older were enrolled between November 1, 1995 and January 31, 1998 to take part in SHHS Visit 1. During exam cycle 3 (January 2001- June 2003), a second polysomnogram (SHHS Visit 2) was obtained in 3,295 of the participants. CVD Outcomes data were monitored and adjudicated by parent cohorts between baseline and 2011. More than 130 manuscripts have been published investigating predictors and outcomes of sleep disorders.

QUT Research Data Respository Dataset and Resources

The DREAMS Databases and Assessment algorithm

During the DREAMS project funded by Région Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.

These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.

In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):

• The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;

• The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1's SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.

• The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;

• The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;

• The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;

• The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.

We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.

(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)

All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst

The data includes polysomnography (PSG) data collected from both children and adults during full night, restricted night and alternate sleep schedules. Data includes standard recording of electroencephalography, electromyography and electrooculography, and in some cases a further combination of electrocardiography and respiratory measures.

README

The Bitbrain Open Access Sleep (BOAS) dataset.

Overview

This project aimed at bridging the gap between gold-standard clinical sleep monitoring and emerging wearable EEG technologies. The dataset contains data from 128 nights in which participants were simultaneously monitored with two technologies: a Brain Quick Plus Evolution PSG system by Micromed and a wearable EEG headband by Bitbrain. The Micromed PSG system records a comprehensive and clinically validated set of physiological sleep parameters, while the Bitbrain wearable EEG headband offers a user-friendly, self-administered alternative, limited to forehead EEG electrodes, movement sensors, and photo-plethysmography. Data from both systems were acquired simultaneously, allowing for direct comparison and validation of the wearable EEG device against the established PSG standard. This dual-recording approach provides a rich resource for evaluating the performance and potential of wearable EEG technology in sleep studies.

Human sleep scoring: To ensure robust and reliable sleep staging, we followed a rigorous labeling process. Three expert sleep scorers independently annotated the PSG recordings following criteria developed by the American Academy of Sleep Medicine (AASM) (Berry et al., 2015). From the resulting three scorings, a consensus label was derived: each epoch of sleep data received the label scored by at least two of the scorers. In cases where all three scorers had given different labels, a fourth scorer made the final decision. This consensus labeling approach addresses the inherent variability in human-derived sleep scoring, with an estimated inter-scorer agreement of approximately 85% (Danker-Hopfe et al., 2009; Rosenberg and Van Hout, 2013).

Automatic scoring: We used the human expert consensus labels to train a deep learning model (Esparza-Iaizzo et al., 2024). By implementing a cross-validation procedure, we trained and validated the model separately on the PSG and wearable EEG datasets. The model achieved an 87.13% match between human-consensus and network-provided labels for the PSG data, and an 86.71% match for the wearable EEG data.

Our dataset includes:

1. PSG recordings from 128 nights (files ending with "*psg_eeg.edf*"),
2. Wearable EEG recordings from the same nights (files ending with "*headband_eeg.edf*"),
3. Human-consensus sleep stage labels, obtained from the PSG recordings ("*stage_hum*" in the PSG data's event files),
4. AI-generated sleep stage labels, separately obtained from PSG recordings and from wearable EEG recordings ("*stage_ai*" in both the PSG and headband data's event files).
5. Further meta data for each recording (i.e., the participants' age, sex, and BMI, provided in the file "*participants.tsv*")

Participants

Participants were members of the general population, provided written informed consent, and received economic compensation of 50€ per night.

In order to represent the general population, we recruited a broad spectrum of participants along the dimensions of age, sex, and body mass index. We did not recruit patients with particular health conditions but only excluded severe conditions that could have affected the feasibility or safety of the study. In detail, inclusion and exclusion criteria were as follows.

Inclusion criteria - Age > 18 years, - Sufficient knowledge of Spanish to understand the explanatory text, the consent form and study-related instructions.

Exclusion criteria - Current severe medical interventions or medication, - History of severe neurological or psychiatric disorders, - Severe health problems in the last 12 months (especially neurological or cardiac disorders), - Current pregnancy or nursing, - Use of psychotropic medication, benzodiazepines, gamma-hydroxybutyric acid, and similar drugs before or during the study.

Format

The dataset is formatted according to the Brain Imaging Data Structure (BIDS). Please note that while the recordings are named from sub-1 up to sub-128, some come from the same participants. 108 unique individuals participated in the recordings, data of which can be matched using the pid (= unique participant ID) property in the file "*participants.tsv*"

The folder of each recording contains the data recorded with the PSG ("*sub-xx_task-Sleep_acq-psg_eeg.edf*") and with the wearable EEG headband ("*sub-xx_task-Sleep_acq-headband_eeg.edf*").

Channel groups

Not all recordings contain data from all available sensors. The full list of available sensors for each recording can be obtained on the "*channels.tsv*" file. Channels in this file are coded in groups: - PSG_EEG: Electroencephalography recorded with the PSG system. Channels available are F3, F4, C3, C4, O1, O2 (PSG_F3, PSG_F4, PSG_C3, PSG_C4, PSG_O1, PSG_O2). - PSG_EOG: Electrooculography signals recorded with the PSG system. The location of the EOG electrodes was lateral of the eyes; one slightly lower than the participant's left eye and one slightly higher than the participant's right eye (according to AASM guidelines). For recordings containing only one EOG channel (PSG_EOG), the electrodes were recorded as a bipolar derivation. If two EOG channels are present (PSG_EOGR, PSG_EOGL), both electrodes were referenced against the left mastoid. - PSG_EMG: Electromyography signals recorded with the PSG system. Data contain a single EMG channel (PSG_EMG), which is the result of a bipolar derivation of two chin electrodes. - PSG_BELTS: Breathing activity recorded by the PSG system using abdominal and thoracic breathing belts (PSG_ABD, PSG_THOR). - PSG_THER: Respiratory airflow recorded with the PSG system using a thermistor (PSG_THER). - PSG_CAN: Respiratory airflow recorded with the PSG system using a nasal cannula (PSG_CAN). - PSG_PPG: Photopletismographic (PPG) activity recorded with the PSG system. Channels available are pulse (PSG_PULSE), heart beat (PSG_BEAT) and oxygen saturation (PSG_SPO2). - HB_EEG: Electroencephalography recorded with the wearable EEG headband. Headband channels are approximately located at AF7 and AF8 (HB_1, HB_2). - HB_IMU: Movement activity recorded by an Inertial Measurement Unit (IMU) in the headband. Signals are derived from an accelerometer (HB_IMU_1, HB_IMU_2, HB_IMU_3) and gyroscope (HB_IMU_4, HB_IMU_5, HB_IMU_6), both recording signals for all three spatial dimensions. - HB_PULSE: Pulse activity recorded with the wearable EEG headband using a PPG sensor (HB_PULSE).

Sleep staging labels

The sleep stage labels for each recording are coded as events in corresponding event files (stage_hum and stage_ai; see above). Stages are coded as follows: - 0: Wake, - 1: NonREM sleep stage 1 (N1), - 2: NonREM sleep stage 2 (N2), - 3: NonREM sleep stage 3 (N3), - 4: REM sleep, - 8: PSG disconnections (e.g., due to bathroom breaks; human-scored only) - -2: Artifacts and missing data (AI-scored only)

References

Berry, R. B., Brooks, R., Gamaldo, C. E., Harding, S. M., Lloyd, R. M., Marcus, C. L., et al. (2015). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.2. Darien, Illinois.

Danker-Hopfe, H., Anderer, P., Zeitlhofer, J., Boeck, M., Dorn, H., Gruber, G., et al. (2009). Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. J. Sleep Res. 18, 74–84. doi: 10.1111/j.1365-2869.2008.00700.x.

Esparza-Iaizzo, M., Sierra-Torralba, M., Klinzing, J. G., Minguez, J., Montesano, L., and López-Larraz, E. (2024). Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea. bioRxiv, 2024.06.12.597764. doi: 10.1101/2024.06.12.597764.

Rosenberg, R. S., and Van Hout, S. (2013). The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring. J. Clin. sleep Med. 9, 81–87. doi: 10.5664/jcsm.2350.

Contact

If you have any questions or comments, please contact:

Eduardo López-Larraz: eduardo.lopez@bitbrain.com Jens G. Klinzing: jens.klinzing@bitbrain.com

Sleep data summarized by minutes in each stage

Overview The Dream EEG and Mentation (DREAM) database collects and stores metadata about DREAM datasets, and is accessible to the public. DREAM datasets provide polysomnography and associated subjective mentation reports. Some datasets may also contain personally identifiable information about participants, but such information are not stored by the DREAM database. Datasets are contributed to DREAM from many different labs in many different studies and, where possible, made openly accessible in the hope of pushing the fields of sleep, dream, brain-computer interface, and consciousness research forward. If you have data that others in the community might find useful, please consider contributing it to DREAM. Contents The DREAM database consists of a following data tables:

Datasets Data records People

The records in Datasets list all officially accepted DREAM datasets and their summary metadata. Data records lists metadata of each individual datum from these datasets. People provides information on the data contributors, referred to by Key ID in Datasets.

The SS2 subset of the Montreal Archive of Sleep Studies (MASS) cohort is an open-access database of laboratory-based polysomnography (PSG) recordings defined as : 19 subjects (age 23.6±3.7 years, age range: 18-33 years) 8 males (age 24.3±4.2 years, age range: 19-33 years) 11 females (age 23.2±3.5 years, age range: 18-30 years) 19 PSG recordings (whole night) "* PSG.edf" 19 electrodes in the EEG montage reference is computed linked-ear (CLE) 4 EOG channels 1 bipolar EMG 1 ECG channel Respiratory thermistance 19 Sleep staging files "* Base.edf" Sleep stage scoring rules : R&K Page size (s) : 20

This is a dual-mode sleep video database. Please cite the following paper if you wish to use our dataset: Hu M, Zhai G, Li D, et al. Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation[J]. PloS one, 2018, 13(1): e0190466.If you have any questions, you can send a request to: humenghan89@163.com

ISRUC-Sleep is a polysomnographic (PSG) dataset. The data were obtained from human adults, including healthy subjects, and subjects with sleep disorders under the effect of sleep medication. The dataset, which is structured to support different research objectives, comprises three groups of data: (a) data concerning 100 subjects, with one recording session per subject, (b) data gathered from 8 subjects; two recording sessions were performed per subject, which are useful for studies involving changes in the PSG signals over time, (c) data collected from one recording session related to 10 healthy subjects, which are useful for studies involving comparison of healthy subjects with the patients suffering from sleep disorders.

Comprehensive dataset of 8,395 Sleep clinics in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This provides data and code to accompany the Ordinal Sleep Depth (OSD), a data-driven continuous measure of sleep depth developed using deep learning. In the manuscript, we evaluate OSD's correlation with arousal probability and its association with age, sex, sleep-disordered breathing (SDB), and cognitive impairment using 21,787 polysomnography recordings from 18,116 unique patients. OSD shows a strong linear correlation with arousal probability (Pearson's r = 0.994), slightly outperforming the Odds Ratio Product (ORP) measure (r = 0.923). Both measures reflect expected decreases in sleep depth with advancing age and demonstrate that females have significantly deeper sleep than males. OSD more accurately captures sleep depth reductions associated with SDB and increasing levels of cognitive impairment.

MIT-BIH Polysomnographic Database is a collection of recordings of multiple physiologic signals during sleep. Subjects were monitored in Boston''s Beth Israel Hospital Sleep Laboratory for evaluation of chronic obstructive sleep apnea syndrome, and to test the effects of constant positive airway pressure (CPAP), a standard therapeutic intervention that usually prevents or substantially reduces airway obstruction in these subjects. The database contains over 80 hours'' worth of four-, six-, and seven-channel polysomnographic recordings, each with an ECG signal annotated beat-by-beat, and EEG and respiration signals annotated with respect to sleep stages and apnea

This repository duplicates the entire Sleep and Dream Database (SDDb), a public collection of dream reports. The purpose of this repository is (a) to provide a convenient access point for the dream reports, and (b) to provide a system of SDDb version control so that analysis of these dream reports can be replicated even when the official SDDb undergoes modifications. It contains all SDDb dream reports as of the date of download (see "Dates" section of this repository). No additional processing was applied.

Comprehensive dataset of 37 Sleep clinics in Delaware, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.