Sleep Quality

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. Source: https://www.physionet.org/content/sleep-edfx/1.0.0/

MaSS (Multilingual corpus of Sentence-aligned Spoken utterances) is an extension of the CMU Wilderness Multilingual Speech Dataset, a speech dataset based on recorded readings of the New Testament. MaSS extends it by providing a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). The covered languages are: Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish. Source: MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible Image Source: https://arxiv.org/pdf/1907.12895v3.pdf

The Montreal Archive of Sleep Studies (MASS) is an open-access and collaborative database of laboratory-based polysomnography (PSG) recordings O’Reilly, C., et al. (2014) J Seep Res, 23(6):628-635. Its goal is to provide a standard and easily accessible source of data for benchmarking the various systems developed to help the automation of sleep analysis. It also provides a readily available source of data for fast validation of experimental results and for exploratory analyses. Finally, it is a shared resource that can be used to foster large-scale collaborations in sleep studies. MASS is composed of cohorts themselves comprising subsets. Recordings within subsets is kept as homogeneous as possible, whereas it is more heterogeneous between subsets. To allow inter-study comparisons, researchers validating their results on MASS are encouraged to specify which portion of the database they used in their assessment (e.g., MASS-C1 for the whole cohort 1, MASS-C1/SS1-SS3 for subsets 1, 2 and 3 of cohort 1). Currently, the first MASS cohort available is described in O’Reilly, C., et al. (2014) J Seep Res, 23(6):628-635. This cohort comprises polysomnograms of 200 complete nights recorded in 97 men and 103 women of age varying between 18 and 76 years (mean: 38.3 years, SD: 18.9 years). It has been split into five different subsets. Source: Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research

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.

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. Source: https://sleeptight.isr.uc.pt/ Image Source: https://sleeptight.isr.uc.pt/

Multi-Ethnic Study of Atherosclerosis (MESA) is an NHLBI-sponsored 6-center collaborative longitudinal investigation of factors associated with the development of subclinical cardiovascular disease and the progression of subclinical to clinical cardiovascular disease.

Data for this challenge were contributed by the Massachusetts General Hospital’s (MGH) Computational Clinical Neurophysiology Laboratory (CCNL), and the Clinical Data Animation Laboratory (CDAC). The dataset includes 1,985 subjects which were monitored at an MGH sleep laboratory for the diagnosis of sleep disorders. The data were partitioned into balanced training (n = 994), and test sets (n = 989). The sleep stages of the subjects were annotated by clinical staff at the MGH according to the American Academy of Sleep Medicine (AASM) manual for the scoring of sleep. More specifically, the following six sleep stages were annotated in 30 second contiguous intervals: wakefulness, stage 1, stage 2, stage 3, rapid eye movement (REM), and undefined. Certified sleep technologists at the MGH also annotated waveforms for the presence of arousals that interrupted the sleep of the subjects. The annotated arousals were classified as either: spontaneous arousals, respiratory effort related arousals (RERA), bruxisms, hypoventilations, hypopneas, apneas (central, obstructive and mixed), vocalizations, snores, periodic leg movements, Cheyne-Stokes breathing or partial airway obstructions. The subjects had a variety of physiological signals recorded as they slept through the night including: electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG), and oxygen saturation (SaO2). Excluding SaO2, all signals were sampled to 200 Hz and were measured in microvolts. For analytic convenience, SaO2 was resampled to 200 Hz, and is measured as a percentage.

The data consist of 70 records, divided into a learning set of 35 records (a01 through a20, b01 through b05, and c01 through c10), and a test set of 35 records (x01 through x35), all of which may be downloaded from this page. Recordings vary in length from slightly less than 7 hours to nearly 10 hours each. Each recording includes a continuous digitized ECG signal, a set of apnea annotations (derived by human experts on the basis of simultaneously recorded respiration and related signals), and a set of machine-generated QRS annotations (in which all beats regardless of type have been labeled normal). In addition, eight recordings (a01 through a04, b01, and c01 through c03) are accompanied by four additional signals (Resp C and Resp A, chest and abdominal respiratory effort signals obtained using inductance plethysmography; Resp N, oronasal airflow measured using nasal thermistors; and SpO2, oxygen saturation).

MODA is a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects) that was produced by the Massive Online Data Annotation project. Sleep spindles were detected as a consensus of a number of human-expert scorers. With a median number of 5 experts scoring every EEG segment, MODA offers sleep spindle annotations of a quality unseen in previous datasets. The dataset was described and introduced in the following publication: Lacourse, K., Yetton, B., Mednick, S. et al. Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data. Sci Data 7, 190 (2020). https://doi.org/10.1038/s41597-020-0533-4