Quick links for this year's Challenge:
22 February 2019: Publications from the 2018 Challenge are now available.
An unofficial entry from Hongyang Li and Yuanfang Guan (who unfortunately missed the deadline to submit an abstract) achieved a score of 0.55.
If you have any questions or comments regarding this challenge, please post it directly in our Community Discussion Forum. This will increase transparency (benefiting all the competitors) and ensure that all the challenge organizers see your question.
When using the resources on this site, please cite the following publications:
At the end of last year, American scientists Jeffrey Hall, Michael Rosbash and Michael Young received a Nobel Prize in Physiology “for their discoveries of molecular mechanisms controlling the circadian rhythm”—the mechanism that regulates sleep (Osborn, 2017). The precise reasons why humans sleep (and even how much sleep we need) remains a topic of scientific inquiry. Contemporary theorists indicate that sleep may be responsible for learning and/or the clearing of neural waste products (Ogilvie and Patel, 2017).
While the precise reasons why we sleep are not perfectly understood, there is consensus on the importance of sleep for our overall health, and well-being. Inadequate sleep is associated with a wide range of negative outcomes including: impaired memory and learning, obesity, irritability, cardiovascular dysfunction, hypotension, diminished immune function (Harvard Medical School, 2006), depression (Nutt et al, 2008), and quality of life (Lee, 2009). Further studies even suggest causal links between quality of sleep, and important outcomes including mental health.
It follows that improving the quality of sleep could be used to improve a range of societal health outcomes, more generally. Of course, the treatment of sleep disorders is necessarily preceded by the diagnosis of sleep disorders. Traditionally, such diagnoses are developed in sleep laboratory settings, where polysomnography, audio, and videography of sleeping subject may be carefully inspected by sleep experts to identify potential sleep disorders.
One of the more well-studied sleep disorders is Obstructive Sleep Apnea Hypopnea Syndrome (or simply, apnea). Apneas are characterized by a complete collapse of the airway, leading to awakening, and consequent disturbances of sleep. While apneas are arguably the best understood of sleep disturbances, they are not the only cause of disturbance. Sleep arousals can also be spontaneous, result from teeth grinding, partial airway obstructions, or even snoring. In this year’s PhysioNet Challenge we will use a variety of physiological signals, collected during polysomnographic sleep studies, to detect these other sources of arousal (non-apnea) during sleep.
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 goal of the challenge is use information from the available signals to correctly classify target arousal regions. For the purpose of the Challenge, target arousals are defined as regions where either of the following conditions were met:
Please note that regions falling within 10 seconds before or after a subject wakes up, has an apnea arousal, or a hypopnea arousal will not be scored for the Challenge.
We have pre-computed the target arousals for you. They are contained in a sample-wise vector (described below in “Accessing the Data”), marked by “1”. Regions that will not be scored are marked by a “-1”, and regions that will be penalized if marked by your algorithm are marked by “0”. You do not need to recompute these scores.
If you don’t have a BitTorrent client, we recommend Transmission.
The Challenge data repository
contains two directories (
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:
tr03-0005.mat: a Matlab V4 file containing the signal data.
tr03-0005.hea: record header file - a text file which describes the format of the signal data.
tr03-0005.arousal: arousal and sleep stage annotations, in WFDB annotation format.
tr03-0005-arousal.mat: a Matlab V7 structure containing a sample-wise vector with three distinct values (+1, 0, -1) where:
Table 1 lists functions that can be used to import the data into Python, Matlab, and C programs.
Table 1: Functions that can be used to import Challenge data.
|Signal (.mat) and header (.hea) files||wfdb.rdrecord||rdmat||isigopen|
|Arousal annotation files (.arousal)||wfdb.rdann||rdann||annopen|
|Arousal files (.mat)||scipy.io.loadmat||load||libmatio|
Participants should use the provided signal and arousal data to develop
a model that classifies test-set subjects. More specifically, for each
/test, participants must generate a
.vec text file that
describes the probability of arousal at each sample, such as:
0.001 0.000 0.024 0.051
The names of the generated annotation files should match the name of the
test subject. For instance,
test/te09-0094.mat should have a
corresponding file named
Entries must be submitted as a zip file containing:
AUTHORS.txtfile containing the list of authors
LICENSE.txtfile containing the license for your code
.vecfiles described above
To upload your entry, create a PhysioNetWorks account (if you don’t have one), and go to moody-challenge.physionet.org. Entries must be uploaded prior to the deadline in order to be eligible.
Your final algorithm will only be graded for its binary classification
performance on target arousal and non-arousal regions (designated by +1
and 0 in
teNN-NNNN-arousals.mat), measured by the area under the
precision-recall curve. The area is defined as follows:
Note that this is the gross AUPRC (i.e., for each possible value of \(j\), the precision and recall are calculated for the entire test database), which is not the same as averaging the AUPRC for each record.
A Python implementation of the scoring algorithm is available here, and a Matlab/Octave implementation is here.
Two simple example algorithms are provided and may be used as a template for your own submission:
Entrants may have an overall total of up to three submitted entries over both the unofficial and official phases of the competition (see Table 2). Following submission, entrants will receive an email confirming their submission and reporting how well their arousal annotations match those of the held-out test set.
All deadlines occur at noon GMT (UTC) on the dates mentioned below. If you do not know the difference between GMT and your local time, find out what it is before the deadline!
|Start at noon GMT on||Entry limit||End at noon GMT on|
|Unofficial Phase||15 February||1||13 April|
|[Hiatus]||13 April||0||22 April|
|Official Phase||23 April||2||1 September|
|* Wildcard submissions due||15 July|
All official entries must be received no later than noon GMT on Saturday, 1 September 2018. In the interest of fairness to all participants, late entries will not be accepted or scored. Entries that cannot be scored (because of missing components, improper formatting, or excessive run time) are not counted against the entry limits.
To be eligible for the open-source award, you must do all of the following:
Please do not submit analysis of this year’s Challenge data to other Conferences or Journals until after CinC 2018 has taken place, so the competitors are able to discuss the results in a single forum. We expect a special issue from the journal Physiological Measurement to follow the conference and encourage all entrants (and those who missed the opportunity to compete or attend CinC 2018) to submit extended analysis and articles to that issue, taking into account the publications and discussions at CinC 2018.
If your abstract is accepted, you must log in to the conference site and agree that you will attend. Then, you must submit a full article describing your results and mark it as a preprint (for others to read) by September 15th. (Don’t forget that the competition deadline is noon GMT on the 1st September - this deadline will not be extended.)
After agreeing to attend, you must register for the conference, pay the conference fee (prices go up after July ends), and secure a visa if you need one. See the Computing in Cardiology site for more information.
If you need a visa, we strongly suggest you register this week and begin the process. Visas can take months to issue and attendance is mandatory - you cannot receive a prize if you do not attend because defending your work is part of the Challenge. The conference (not PhysioNet) will supply you with a letter for your visa. Please see the CinC 2018 site (archived snapshot) for details on how to obtain that letter and who to contact. If you have any questions about this process, or are concerned about paying the conference fee before securing a visa, please contact the conference organizers, not PhysioNet.
If your abstract is rejected, then you have one more chance! This year we are introducing a ‘wildcard’ submission. On July the 15th, the top scoring entry that has not so far been accepted to CinC will be offered the opportunity to submit another (or a new) abstract to the conference system (containing full results). If the team can submit a quality abstract (with performance results) and register for the conference then it's members will be eligible for a prize (assuming they also attend the conference and present a poster). Don’t forget, your abstract was probably rejected because it didn’t contain any useful results (even on training data) and/or did not describe your methods well. So please pay attention to the abstract when submitting - it won’t be automatic. We strongly believe that if you are unable to explain what you did and why, then the code is of very limited value.
We hope this is a suitable encouragement for teams that are either late to the Challenge or failed to secure a place at the conference to continue with their efforts in the competition. It would be a shame not to see potentially great works at the conference.
Look out for future announcements via the community discussion forum.
As is customary, we hope to run a special issue in Physiological Measurement with a closing date of 31 January 2019. We will therefore encourage competitors (and non-competitors) to submit updates and further reworks based on the Challenge after the award ceremony at the Computing in Cardiology Conference in Maastricht in September.
The MathWorks has kindly decided to sponsor Physionet’s 2018 Challenge providing licenses. The MathWorks is offering to all teams that wish to use MATLAB, complimentary licenses. Users can apply for a license and learn more about MATLAB support through The Mathworks’ PhysioNet Challenge link. If you have questions or need technical support, please contact The MathWorks at firstname.lastname@example.org.
Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362.