PhysioNet/CinC Challenge 2018: Training/Test Sets

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Data for the 2018 PhysioNet/Computing in Cardiology 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). Collected clinical characteristics and outcomes of the patients are presented in Table 1, below.

Clinical Feature Total
(n = 1893)
(n = 994)
(n = 989)
Age 55(+/-14.4) 55(+/-14.3) 55(+/-14.4)
Body Mass Index 33(+/-7.6) 33(+/-7.8) 33(+/-7.5)
Epworth Sleepiness Scale 8.6(+/-5.3) 8.5(+/-5.3) 8.7(+/-5.3)
Gender (% Male) 65 67 63
Drug Use (%)
Antidepressant 26.1 25.7 26.5
Antihistamine 4.8 4.8 4.8
Benzodiazepine 16.1 16.9 15.4
Diabetic 11.7 11.9 11.5
Herbal 4.2 4.3 4.0
Hypertension 40.9 41.0 40.6
Neuroleptic 4.2 4.5 3.8
Opiate 7.4 8.1 6.7
Neuroactive 19.1 20.8 17.5
Sleep aids 28.3 29.0 27.8
Stimulant 4.7 3.9 5.5
Reason For Visit (%)
Diagnostic (%) 41.8 41.16 42.47
Split Night CPAP (%) 38.35 37.95 39.03
All Night CPAP (%) 19.85 20.88 18.5
Table 1: Clinical characteristics of the dataset, and those of the training, and testing set.

Sleep Stages

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. Characteristics of the subjects during sleep are presented in Table 2.

Clinical Feature Overall
(n = 1893)
(n = 994)
(n = 989)
Total Recording Time (hours) 7.7(+/-0.67) 7.7(+/-0.66) 7.7(+/-0.68)
Total Time In bed (hours) 7.5(+/-0.67) 7.5(+/-0.67) 7.5(+/-0.67)
Total Sleep Time (hours) 6.2(+/-1.2) 6.2(+/-1.1) 6.1(+/-1.2)
Sleep Stages %  [mean(std)]
Wake 29.3(+/-59) 28(+/-48) 31(+/-69)
Non-REM 1 19.5(+/-14) 19.6(+/-14.3) 19(+/-13)
Non-REM 2 51.3(+/-12.9) 51(+/-13) 51.7(+/-12.6)
Non-REM 3 13.8(+/-9.8) 14(+/-9.8) 13.8(+/-9.8)
REM 15.3(+/-8.4) 15.5(+/-8.7) 15.2(+/-8.2)
Arousal Indices [mean(std)]
Apnea Hypopnea 19(+/-14.4) 19(+/-14.6) 18.9(+/-14.4)
Respiratory Disturbance 26.2(+/-16.5) 26.3(+/-16.6) 26.2(+/-16.4)
Periodic Limb Movement 24.4(+/-50.7) 24(+/-34.2) 24.8(+/-63.2)
Table 2: Sleep and arousal characteristics of training and testing data.


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.

Target arousals
Bruxism 30
Cheyne-Stokes breathing 3
Hypoventilation 4
Noise 1
Partial airway obstruction 11
Periodic leg movement (PLM) 36
Respiratory effort (RERA) 43,822
Snoring 28
Spontaneous 70
Total 44,005
Non-target arousals
Hypopnea 56,936
Central apnea 22,763
Mixed apnea 2,641
Obstructive apnea 32,547
Total 114,887
Table 3: Number and types of arousals in the training set.


The subjects had a variety of physiological signals recorded as they slept through the night including: electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (ECG), and oxygen saturation (SaO2). In Table 4, we present a full list of the available signals. Six channels of EEG (F3-M2, F4-M1, C3-M2, C4-M1, O1-M2, O2-M1) were collected using the International 10/20 system of electrode placement. Single lead ECG was collected with electrodes placed below the right clavicle near the sternum and over left lateral chest wall. Left eye EOG was collected setting the right ear EEG electrode (M2) as reference. EMG recordings were made at the chin, chest, and abdomen. Excluding SaO2, all signals were sampled to 200 Hz and were measured in microvolts. For analytic convenience, SaO2 was resampled to 200Hz, and is measured as a percentage.

Signal Name Units Signal Description
SaO2 % Oxygen saturation
ABD µV Electromyography, a measurement of abdominal movement
CHEST µV Electromyography, measure of chest movement
Chin1-Chin2 µV Electromyography, a measure of chin movement
AIRFLOW µV A measure of respiratory airflow
ECG mV Electrocardiogram, a measure of cardiac activity
E1-M2 µV Electrooculography, a measure of left eye activity
O2-M1 µV Electroencephalography, a measure of posterior activity
C4-M1 µV Electroencephalography, a measure of central activity
C3-M2 µV Electroencephalography, a measure of central activity
F3-M2 µV Electroencephalography, a measure of frontal activity
F4-M1 µV Electroencephalography, a measure of frontal activity
O1-M2 µV Electroencephalography, a measure of posterior activity
Table 4: Physiological signals available for the prediction of arousals.

For compression purposes, all signals were converted from 64 bit float format into 16 bit signed int using the scale and offset approach. Data for the challenge are stored in Matlab-compatible WFDB signal files.

Accessing the Data

Data for the challenge may be browsed below, or viewed online using LightWAVE. The data repository contains two directories (training and test) which are each approximately 135 GB in size. Each directory contains one subdirectory per subject (e.g. training/tr03-0005). Each subdirectory contains signal, header, and arousal files; for example:

  1. tr03-0005.mat: a Matlab V4 file containing the signal data.
  2. tr03-0005.hea: record header file - a text file which describes the format of the signal data.
  3. tr03-0005.arousal: arousal and sleep stage annotations, in WFDB annotation format.
  4. tr03-0005-arousal.mat: a Matlab V7 structure containing a vector of sleep stages and target arousal events for the Challenge, sampled at 200 Hz.

Table 5 lists functions that can be used to import the data into Python, Matlab, and C programs.

Table 5: Functions that can be used to import Challenge data.
File type Python Matlab C / C++
Signal (.mat) and header (.hea) files wfdb.rdrecord rdmat isigopen
Arousal annotation files (.arousal) wfdb.rdann rdann annopen
Arousal files (.mat) load libmatio


If you don't have a BitTorrent client, we recommend Transmission.

Icon  Name                            Last modified      Size  Description
[PARENTDIR] Parent Directory - [   ] ANNOTATORS 2018-02-20 12:08 34 list of annotators [   ] RECORDS 2018-02-20 14:48 52K list of record names [TXT] age-sex.csv 2018-03-26 19:23 29K [DIR] test/ 2018-02-20 13:30 - [DIR] training/ 2018-05-07 16:31 -

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Updated Friday, 28 October 2016 at 16:58 EDT

PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.