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Institute of Biomedical Imaging and Bioengineering (NIBIB) U24EB037545","National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB030362","The MathWorks (equipment sponsorship)"],"ingestion_fingerprint":"a845cbf27a3bd8b39b1cacedc5672abdc0c30f1a42f8d63b8f5434a35d9da219","license":"Open Data Commons Attribution License v1.0","n_contributing_labs":null,"name":"PhysioNet 2018 Challenge: Sleep Arousal Detection PSG (Training)","readme":"You Snooze You Win: PhysioNet/CinC Challenge 2018 PSG\n=======================================================\nOverview\n--------\n1,983 overnight polysomnographic (PSG) recordings from subjects monitored at\nthe Massachusetts General Hospital (MGH) sleep laboratory for sleep disorder\ndiagnosis. The dataset was created for the PhysioNet/Computing in Cardiology\nChallenge 2018 on automatic arousal detection.\n- Training set: 994 subjects (with expert annotations)\n- Test set: 989 subjects (PSG signals only, no annotations)\n- Demographics: mean age 55 +/- 14 years (range 18-93), 65% male, 35% female\n- Clinical population: subjects with suspected obstructive sleep apnea\nChannels (13 total, all at 200 Hz)\n-----------------------------------\n- EEG (6): F3-M2, F4-M1, C3-M2, C4-M1, O1-M2, O2-M1\n  Referential montage against contralateral mastoids (M1/M2)\n- EOG (1): E1-M2 (left electrooculogram)\n- EMG (1): Chin1-Chin2 (submental chin electromyogram)\n- Respiratory (3): ABD (abdominal effort), CHEST (thoracic effort),\n  AIRFLOW (nasal/oral airflow)\n- SpO2 (1): SaO2 (pulse oximetry, resampled to 200 Hz)\n- ECG (1): ECG (single-lead electrocardiogram)\nAnnotations (training set only, in events.tsv)\n------------------------------------------------\nSleep staging (AASM standard, 30-second contiguous epochs):\n  Wake, N1, N2, N3, REM\nRespiratory events (with onset and duration):\n  resp_obstructiveapnea  — complete upper airway obstruction\n  resp_centralapnea      — absent respiratory effort\n  resp_mixedapnea        — combined obstructive + central\n  resp_hypopnea          — partial airway obstruction (>=30% flow reduction)\nArousal events:\n  arousal_rera           — respiratory effort-related arousal\n  arousal_spontaneous    — spontaneous cortical arousal\n  arousal_snore          — snoring-related arousal\n  arousal_plm            — periodic leg movement arousal\nParticipants metadata (in participants.tsv)\n--------------------------------------------\nPer-subject: age, sex, split (training/test), recording duration, sleep\narchitecture (epoch counts per stage), and respiratory/arousal event counts.\nSessions\n--------\n- ses-training: 994 subjects with PSG + annotations\n- ses-test: 989 subjects with PSG only (no annotations)\nNotes\n-----\n- Original format: WFDB (.mat + .hea + .arousal)\n- All signals originally at 200 Hz; SaO2 was resampled to match\n- Annotators: certified sleep technologists at MGH, following AASM manual\n- Updated arousal annotations (new-arousals.zip) supersede originals\nReference\n---------\nGhassemi, M.M., Moody, B.E., Lehman, L.H., Song, C., Li, Q., Sun, H.,\nMark, R.G., Westover, M.B. & Clifford, G.D. (2018). You Snooze, You Win:\nthe PhysioNet/Computing in Cardiology Challenge 2018. Computing in\nCardiology, 45, 1-4. doi:10.22489/CinC.2018.049\nGoldberger, A. et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet:\nComponents of a new research resource for complex physiologic signals.\nCirculation, 101(23), e215-e220.\nhttps://physionet.org/content/challenge-2018/1.0.0/\nReferences\n----------\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8","recording_modality":["eeg"],"senior_author":null,"sessions":["test","training"],"size_bytes":430724611756,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000225","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["sleep"],"timestamps":{"digested_at":"2026-04-30T14:09:11.120740+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-10T10:05:10Z"},"total_files":1983,"computed_title":"PhysioNet 2018 Challenge: Sleep Arousal Detection PSG (Training)","nchans_counts":[{"val":13,"count":1983}],"sfreq_counts":[{"val":200.0,"count":1983}],"stats_computed_at":"2026-05-01T13:49:34.660008+00:00","total_duration_s":54940432.085,"author_year":"Ghassemi2018"}}