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.

Polysomnographic data from the Sleep-EDF dataset, which contains 197 full-night polysomnographic recordings.

Dataset

This dataset was created by Nizar Islah

Contents

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 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

The experimental results of Sleep-EDF data set are compared with the existing research results.

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).

EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:

'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'

Confusion matrix and per-class performance achieved by the proposed method using Fpz-Cz EEG channel of the EDF-Sleep-2013 database.

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

README

The Bitbrain Open Access Sleep (BOAS) dataset.

Overview

This project aimes at bridging the gap between gold-standard clinical sleep monitoring and emerging wearable EEG technologies. The dataset comprises 128 nights in which healthy 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 provides a comprehensive and clinically validated set of sleep parameters, while the Bitbrain wearable EEG headband offers a user-friendly, self-administered alternative, limited to forehead EEG electrodes.

A relevant aspect of the dataset is the simultaneous acquisition of data from both systems, 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.

To ensure robust and reliable sleep staging, we employed a rigorous labeling process. Three expert sleep scorers independently annotated the PSG recordings following the criteria developed by the American Academy of Sleep Medicine (AASM) (Berry et al., 2015), and a consensus label was derived from these annotations by a fourth expert. This consensus labeling approach addresses the inherent variability in human sleep staging, which has an estimated inter-scorer agreement of approximately 85% (Danker-Hopfe et al., 2009; Rosenberg and Van Hout, 2013). The consensus labels were then applied to the corresponding wearable EEG recordings, leveraging the simultaneous data acquisition. Moreover, we utilized a deep learning model to analyze the dataset (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 87.08% match between the human-consensus labels and the network-provided labels for the PSG data, and an 86.64% match for the wearable EEG data.

Our dataset, therefore, includes:

1. Raw and labeled PSG recordings from 128 nights.

2. Raw and labeled wearable EEG recordings from the same nights.

3. Human-consensus sleep stage labels for both PSG and wearable EEG data.

4. AI-generated sleep stage labels for both datasets.

Format

The dataset is formatted according to the Brain Imaging Data Structure.

The folder of each participant 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). Note that not all the PSG sensors were used with all the participants. The full list of available sources of activity for each recording can be obtained on the 'channels.tsv' file.

Meaning of all the channel groups:

- PSG_EEG: EEG signals 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: EOG signals recorded with the PSG system. Some of the participants have just one EOG derivation (PSG_EOG), whereas others have 2 lateral derivations, referenced to the mastoid (PSG_EOGR;PSG_EOGL).
- PSG_EMG: EMG signals recorded with the PSG system. One chin EMG derivation is available (PSG_EMG).
- PSG_BELTS: Breathing activity recorded the PSG system using breathing belts. Abdominal and thoracic breathing belts were used (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 activity recorded with the PSG system. Channels available are pulse (PSG_PULSE), heart beat (PSG_BEAT) and oxygen saturation (PSG_SPO2).

- HB_EEG: EEG signals recorded with the wearable EEG headband. The headband measuring locaations are equivalent to AF7 and AF8 (HB_1;HB_2).
- HB_IMU: Movement activity recorded from an accelerometer and gyroscope. Both sensors record in 3 dimensions (x, y, z) each, (HB_IMU_1;HB_IMU_2;HB_IMU_3;HB_IMU_4;HB_IMU_5;HB_IMU_6)
- HB_PULSE: Pulse activity recorded with the wearable EEG headband using a PPG sensor (HB_PULSE).

The sleep stages of each night are coded as events at the corresponding recording folder. The sleep stages obtained as the consensus of the three experts, as well as the labels obtained by the AI using the EEG activity recorded with the PSG can be found in the 'sub-xx_task-Sleep_acq-psg_events.tsv' files. The sleep stages obtained by the AI using the EEG activity recorded with the wearable headband can be found in the 'sub-xx_task-Sleep_acq-headband_events.tsv' files.

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

Dataset of EEG recordings containing HFO markings for 30 pediatric patients with epilepsy

Summary

High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the pediatric population. We recorded and processed the first 3 hours of sleep EEG data in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. The software for the detection of HFOs is freely available at the GitHub repository (https://github.com/ZurichNCH/Automatic-High-Frequency-Oscillation-Detector). Furthermore HFO markings are also added in this database for the selected N3 intervals.

Repository structure

Main directory (hfo/)

Contains metadata files in the BIDS standard about the participants and the study. Folders are explained below.

Subfolders

• hfo/sub-**/ Contains folders for each subject, named sub-
• hfo/sub-**/ses-01/eeg Contains the raw eeg data in .edf format for each subject. The duration is typically 3 hours, that was recorded in the beginning of the sleep. Details about the channels are given in the corresponding .tsv file.

• hfo/derivatives Besides containingsubfolders for the raw data, there are two .json files. The events_description.json explains the meaning of the columns of the event description tsv files (in the subfolders). The interval_description.json explains the meaning of the columns of the interval description tsv files (in the subfolders).

• hfo/derivatives/sub-**/ses-01/eeg/ Contains processed data for each subject. Based on the sleep annotations, first we identified the sleep stages. Then we cut 5 minutes data intervals from the N3 sleep stages. We applied bipolar referencing by considering all nearest neighbour chanels, thus resulting in 52 bipolar channels. Each run corresponds to one 5 minute data interval. The DataIntervals.tsv file provides information about how the various runs are related to the raw data by providing the start and end indeces. Besides the .edf and channel descriptor .tsv files there is an other .tsv file containing the detected candidate event details. Eg. sub-26_ses-01_task-hfo_run-01_events.tsv contains for subject 26 for the first processed data interval the event markings as indeces with additional features of this event described in the abovementioned events_description.json file.

Related materials

The code for HFO detection is available at https://github.com/ZurichNCH/Automatic-High-Frequency-Oscillation-Detector

Support

For questions on the dataset or the task, contact Johannes Sarnthein at johannes.sarnthein@usz.ch.

Recall rate of each sleep stage classification under different algorithms under label smoothing on UCD dataset.

The National Sleep Research Resource (NSRR) offers free web access to large collections of de-identified physiological signals and clinical data elements collected in well-characterized research cohorts and clinical trials. Using the tools provided, the researcher can search across thousands of data elements, identify those data of most relevance for given needs, explore the statistical distributions of each, and download the data as CSV files. Data include demographic, physiological, clinical, and other data types collected by each study. Physiologic signals from overnight polysomnograms (sleep studies) are available by downloading European Data Format (EDF) files. The researcher can load summary measures of standardly scored sleep data. Specific scored annotations can be accessed by downloading XML files and can be viewed offline using the EDF Viewer.

Sleep -EDF sleep staging.

PIR data and EEG-scored sleep in the Wellcome Open Research article:

'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'

1sensorPIRvsEEGdata.csv - PIR based actigraphy for mice to compare to EEG-scored sleep

EEG_4mice10sec.csv - Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118

blandAltLandD.csv - paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)

1monthPIRsleep.csv - 1 month of activity for for figure 4

24mice_activity_LD1week.csv - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)
24mice_sleep_LD1week.csv

This dataset contains edf files (folder "APNEA_EDF") comprising polysomnogram signals for 212 patients, rml files (forlder "APNEA_RML") containing all annotations by the medical team of Sismanoglio General Hospital of Athens and additional rml files (folder "APNEA_RML_clean") containing the annotations of the medical team after automatic rejection of the false positive apneas only. Each patient contains a unique subfolder in folder "APNEA_EDF" and a unique .rml file in folder "APNEA_RML", named with a number in the range 995-1701.

Dream EEG and Mentation (DREAM) data set

Data set information

• Common name: TWC_USA
• Full name: Two-way communicaton data from USA team
• Authors: Karen R. Konkoly, Remington Mallett, Ken A. Paller
• Location: Northwestern University
• Year: 2021
• Set ID: 4
• Amendment: 1
• Corresponding author ID: 4

Previous publications:
Konkoly, K. R., Appel, K., Chabani, E., Mangiaruga, A., Gott, J., Mallett, R., ... & Paller, K. A. (2021). Real-time dialogue between experimenters and dreamers during REM sleep. Current Biology, 31(7), 1417-1427.

Correspondence:
karenkonkoly2023@u.northwestern.edu

Metadata

• Key ID: 5
• Date entered: 2023-02-08T03:09:10+00:00
• Number of samples: 33
• Number of subjects: 19
• Proportion REM: 61%
• Proportion N1: 18%
• Proportion N2: 18%
• Proportion W: 0%
• Proportion experience: 82%
• Proportion no-experience: 15%
• Proportion healthy: 100%
• Provoked awakening: Some
• Time of awakening: Mixed
• Form of response: Structured
• Date approved: 2023-02-09T05:50:12+00:00

How to decode data files

• L-MSTD is an electrode on the left mastoid for if a back-up reference channel was needed. The EMG channel is on the chin, and channels 26 and 27 are back-up EMG electrodes located nearby on the chin. On some recordings EMG-2 is the back-up EMG channel instead, which is also located nearby on the chin.
• The "status" channel was created when converting the data to EDF, and contains information about port codes in the data set. Disregard that the port codes are expressed in microvolts. More information about the meanings of the port codes below.
• It may be that there is a second of flat EEG at the end of each recording which appears to be an artifact of converting the file type to .EDF and should be disregarded

• The time of awakening column contains only approximated times based on experimenters' notes and the duration of files

• There are port codes in the data that have slightly meanings for some different participants (in the "status" channel). Here is a guide for their meanings:

  • 32 just indicates that a new script was started (no sounds played)
  • 64 means the volume was turned down (no sounds played)
  • 65 means the volume was turned up (no sounds played)
  • 29 means a TLR auditory cue was presented
  • 23 means a TLR light cue was presented (but code and light cue are triggered manually, so time-locked analyses is not possible here)
  • Codes 1-20 correspond to math problems that were presented during sleep, and were changed after case 08. See below for guide

  Cases 01-08

  • 1: 9-7
  • 2: 3+2
  • 3: 14-13
  • 4: 6+1
  • 5: 19-16
  • 6: 1+1
  • 7: 5-2
  • 8: 1+4
  • 9: 15-10
  • 10: 3+3
  • 11: 8-4
  • 12: 2+2
  • 13: 8-0
  • 14: 4+1
  • 15: 14-13
  • 16: 2+4
  • 17: 16-13
  • 18: 3+1
  • 19: 10-8
  • 20: 1+0

  Cases 09-33

  • 1: 9-7
  • 2: 3+1
  • 3: 8-7
  • 4: 1+2
  • 5: 9-6
  • 6: 1+1
  • 7: 5-2
  • 8: 4-1
  • 9: 8-6
  • 10: 8-5
  • 11: 2+2
  • 12: 2+1
  • 13: 3+0
  • 14: 1+0
  • 15: 7-4
  • 16: 2+0
  • 17: 6-3
  • 18: 3-1
  • 19: 5-4
  • 20: 1+0

Treatment group codes

N/A

Experimental description

Methods:

Twenty-two participants (15 female, age range 18-33 years, M = 21.1 ± 4.3 years) who claimed to remember at least one dream per week were recruited by word of mouth, online forum, and the Northwestern University Psychology Department participant pool. They each participated in one or more nap sessions, which amounted to 27 nap sessions in total.

Procedure:

Participants visited the laboratory at Northwestern University at approximately their normal wake time and received guidance on identifying lucid dreams and instructions for the experiment for about 40 min during preparations for polysomnographic recordings, including EEG, EMG, and EOG, using a Neuroscan SynAmps system. Participants were instructed to signal with a prearranged number of LR eye movements if they became lucid in a dream.

Next, participants practiced making ocular signals and responding to questions using combinations of LR signals. Subsequently, participants completed the Targeted Lucidity Reactivation (TLR) procedure while lying in bed. This procedure was derived from the procedure developed by Carr and collegues. A method of reality checking to induce lucid dreaming was paired with sensory stimulation and accelerated in a single session immediately before sleep, and then cues were presented again during REM sleep. In this procedure, participants were trained to associate a novel cue sound with a lucid state of mind during wake. The sound consisted of three pure-tone beeps increasing in pitch (400, 600, and 800 Hz) at approximately 40-45 dB SPL and lasting approximately 650 ms. For one participant (ppt. 121) the pure-tone beeps had previously been associated with a different task in an unrelated study. Thus, for this participant, a 1000-ms violin sound and low-intensity flashing-red LED lights were used as cues. All participants were informed that this cue would be given during sleep to help promote a lucid dream. Over the next 15 min, the TLR sound was played up to 15 times. The first 4 times, it was followed by verbal guidance to enter a lucid state as follows. ‘‘As you notice the signal, you become lucid. Bring your attention to your thoughts and notice where your mind has wandered.[pause] Now observe your body, sensations, and feelings.[pause] Observe your breathing. [pause] Remain lucid, critically aware, and notice how aspects of this experience are in any way different from your normal waking experience.’’

Participants often fell asleep before all 15 TLR cue presentations were completed. Standard polysomnographic methods were used to determine sleep state. Once participants entered REM sleep, TLR cues were presented again, at about 30-s intervals, as long as REM sleep remained stable. After participants responded to a cue with a lucid eye signal, or after approximately 10 cues were presented without response, we began the math problem portion of the experiment.

We devised the following task to engage auditory perception of math problems, working memory, and the ability to express the correct answer. We used simple addition and subtraction problems that could each be answered by a number between 1 and 4 (LR = 1, LRLR = 2, LRLRLR = 3, LRLRLRLR = 4), or between 1 and 6 for the first 5 participants.

From the above dataset, data was included in DREAM if there was a period of sleep on the EEG followed by a report of a dream (or a lack of dream). The EEG data includes the last period of continuous sleep before the dream report was collected, starting with the first epoch scored as wake, and ending at the last second before clear movement/alpha activity indicating wake. Also, there are a few instances, noted in the “Remarks” column in the “Records” file, where I included epochs that were scored as wake, when the wake seemed due to alpha from participants attempting to answer questions with eye movements (only one of these included wake in the last 20 seconds of the recording, case21_sub111).

EEG sleep data was NOT included if it was not followed by a verbal/written dream report or clear note on the experimenter’s log that there was no recall. Also not included is data where participants completed eye signals or answered questions, but it was not part of the continuous period of sleep before a dream report was given. Also excluded was a case in which a dream report was collected at the end of the nap but the participant had been in and out of sleep beforehand, so it was unclear which sleep period the report referred to.

DREAM categorization procedure

Karen Konkoly rated reports according to the DREAM categorization. If the participant reported remembering any sort of mental content from sleep, it was rated “2”. If the participant reported remembering a dream but none of its content, it was rated “1”. If the participant reported not remembering anything, or not falling asleep, it was rated “0”.

Recall rate of each sleep stage classification under different algorithms under label smoothing on SHHS dataset.

Kappa coefficient under different algorithms.