You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018

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.

Introduction:

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.

Challenge Data

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.

Objective of the Challenge

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.

Accessing the Data

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

The Challenge 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 sample-wise vector with three distinct values (+1, 0, -1) where:
    • +1: Designates arousal regions
    • 0: Designates non-arousal regions
    • -1: Designates regions that will not be scored

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.
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) scipy.io.loadmat load libmatio

Submitting your Entry

Participants should use the provided signal and arousal data to develop a model that classifies test-set subjects. More specifically, for each subject in /test, participants must generate a .vec text file that describes the probability of arousal at each sample, such as:

0.01
0.00
0.02
0.05

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 annotations/te09-0094.vec.

Scoring

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.

Sample Submission

In the coming days, we will release a baseline algorithm, and accompanying scripts for the challenge. Please check for updates.

Rules and Deadlines

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!

Table 2: Rules and deadlines.
Start at noon GMT on Entry limit End at noon GMT on
Unofficial Phase 15 February 1 9 April
[Hiatus] 9 April 0 15 April
Official Phase 16 April 2 1 September

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:

  1. Submit at least one open-source entry that can be scored before the Phase I deadline (noon GMT on Monday, 9 April 2018).
  2. Submit at least one entry during the second phase (between noon GMT on Monday, 16 April 2018 and noon GMT on Saturday, 1 September 2018). Only your final entry will count for ranking.
  3. Entering an Abstract to CinC: Submit an acceptable abstract (about 299 words) on your work on the Challenge to Computing in Cardiology no later than 15 April 2018. Include the overall score for your Phase I entry in your abstract. Please select “PhysioNet/CinC Challenge” as the topic of your abstract, so it can be identified easily by the abstract review committee. You will be notified if your abstract has been accepted by email from CinC during the first week in June.
  4. Submit a full (4-page) paper on your work on the Challenge to CinC no later than the deadline of conference paper submission.
  5. Attend CinC 2018 (23-26 September 2018) in Maastricht and present your work there.

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.

Abstract Submission

Don't forget you must also submit an abstract to Computing in Cardiology before the imminent deadline on the 15th of April and attend the conference in September where we will announce the winner. See cinc.org. Note that your methods and score, and therefore your abstract, will most likely change by the end of summer. That is acceptable and expected. If you don't submit now though, you won't reserve your place to discuss your methods at the conference. Please note that abstracts should include your current methods and score. We encourage you to include cross validation stats on the training data too to show the reviewers you know what you are doing. In many ways toys is far more important than your test set score we give you at this stage, so if you have a poor challenge score but a great cross validated score then you are well on your way!

Incoherent or information poor abstracts are unlikely to be accepted as they indicate low quality approaches and an inability to communicate ideas. A well thought out abstract indicates a high likelihood of a good presentation and high quality scientific approach. Do not include a description of the competition in the abstract (it's very clear what the competition is about.) Focus on your methods and results.

Please make sure you select the PhysioNet Challenge category in the abstract submission. If you don't, it may get reviewed outside of the challenge track and get rejected.

Please give your abstract and paper a title distinct from that of the Challenge itself (“You Snooze, You Win: The PhysioNet/Computing in Cardiology Challenge 2018.”)

After the Challenge

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.

Obtaining complimentary MATLAB licenses

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. User 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 studentcompetitions@mathworks.com.

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