2018 News

The EEGMAT database contains EEG recordings of subjects before and during the performance of mental arithmetic tasks.

ECG effects of ranolazine, verapamil, lopinavir+ritonavir, chloroquine, dofetilide, diltiazem, and dofetilide+diltiazem in a small sample size clinical study. The ECGCIPA database contains multi-channel ECG recordings of 60 subjects participating in the CiPA ECG validation study.

• The Journal of Physiological Measurement is now accepting submissions in the focus issue: Automated Analysis of Arousals, Sleep and Sleep-Related Disorders Using Physiological Time Series. Please included updated results from the test dataset if you develop a new algorithm. Note that the scope does not confine you to the data and topic of this year's Challenge. Please see here for more information on the scope and how to submit. The deadline is set for the end of February 2019.

The advent of telehealth applications and remote patient monitoring has led to an increasing need for continuous signal quality monitoring to ensure high diagnostic accuracy of the recordings. Cardiovascular diseases often manifest electrophysiological anomalies, therefore the electrocardiogram (ECG) is one of the most used signals for diagnostic applications. Various types of noise and artifacts are not uncommon in ECG recordings and assessing the quality of the signal is essential prior to any clinical interpretation. In this study, a dynamic signal quality index (dSQI) is introduced using a new time-frequency template-based approach.

Functional metrics of autonomic control of heart rate, including baroreflex sensitivity, have been shown to be strongly associated with cardiovascular risk. A decrease in baroreflex sensitivity with aging is hypothesized to represent a contributing causal factor in the etiology of primary hypertension. To assess baroreflex function in human subjects, two complementary methods to simulate the response in heart rate elicited by the Valsalva maneuver were developed and applied to data obtained from a cohort of healthy normal volunteers.

• Official results, as well as a paper describing the Challenge, are now available. Top scores were achieved by
  1. Matthew Howe-Patterson, Bahareh Pourbabaee, and Frederic Benard (0.54)
  2. Guðni Fannar Kristjansson, Heiðar Már Þráinsson, Hanna Ragnarsdóttir, Bragi Marinósson, Eysteinn Gunnlaugsson, Eysteinn Finnsson, Sigurður Ægir Jónsson, Halla Helgadóttir, and Jón Skírnir Ágústsson (0.45)
  3. Runnan He, Kuanquan Wang, Yang Liu, Na Zhao, Yongfeng Yuan, Qince Li, and Henggui Zhang (0.43)
• An unofficial entry from Hongyang Li and Yuanfang Guan (who unfortunately missed the deadline to submit an abstract) achieved a score of 0.55.

The CVES database contains multimodal data from a large study investigating the effects of ischemic stroke on cerebral vasoregulation. The cross sectional study compared 60 subjects who suffered strokes, to 60 control subjects, collecting the following data for each patient across multiple days: transcranial doppler of cerebral arteries, 24-h blood pressure numerics, high resolution waveforms (ECG, blood pressure, CO2 and respiration) during various movement tasks, 24-h ECG, EMG, and accelerometer recordings, and gait pressure recordings during a walking test.

• Challenge entry submission is now closed. Authors must select the entry they wish to be run against the full Challenge data set before 10 Sept 2018 (midnight GMT, 7pm EDT). After this deadline the most recent entry (only) will be selected and evaluated on the full test data.
• Winners will be announced and prizes presented at Computing in Cardiology on 26 Sept 2018.

The Complex Upper-Limb Movements database contains hand trajectory data from ten subjects undergoing writing tasks, used to model motor primitives.

The Term-Preterm ElectroHysteroGram DataSet with Tocogram (TPEHGT DS) contains 26 four-signal 30-min uterine EHG records, i.e., three EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal) of pregnant women, and another five 30-min uterine records (EHG signals and TOCO signal) of non-pregnant women.

The sleep-edf database has been expanded to contain 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature.

This dataset contains signals and numerics extracted from the much larger MIMIC-II matched waveform Database, along with manual breath annotations made from two annotators, using the impedance respiratory signal.

• CinC abstract acceptances/rejections have now been sent out.
• If your abstract was rejected, do not be despondent - please see the Rules and Deadlines below for a second chance to have your abstract accepted for and stay in the official running for the Challenge. This is chance is open to everyone, not just those with rejected abstracts.
• If your abstract was accepted, please 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)
• If you will need a visa to attend CinC, please do this immediately, and follow these instructions.

This database includes 208 voice samples, from 150 pathological, and 58 healthy voices.

The PhysioNet Cardiovascular Signal Toolbox is an open-source modular program for calculating heart rate variability (HRV) implemented in Matlab with evidence-based algorithms and output formats. The Toolbox is compatible with 64-bit MATLAB on GNU/Linux, Mac OS X, and MS-Windows.

• Top scores in the Unofficial Phase were achieved by Matthew HP and Bahareh Pourbabaee with a score of 0.439, Yang Liu and Runnan He with a score of 0.244, and Márton Görög, Bálint Varga, and Péter Hajas with a score of 0.228.
• An updated set of arousal files are now available.
• A Matlab implementation of the scoring function is now available.
• Both the Python and Matlab sample submissions have been updated to use the new scoring code.

These software scripts calculate Global Electrical Heterogeneity scores of ecg signals, which characterize the degree of heterogeneity of the total recovery time across the ventricles.

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.

• A Python implementation of the scoring function (gross area under precision-recall curve) is available here.

• A second sample submission, implemented using Matlab, has been posted.
• Be sure to submit your abstract for Computing in Cardiology before April 15! See the CinC site for more information. Please submit an abstract with your training results, even if you have not yet been able to obtain a score on the test set.

• A sample submission and scoring function are now online.
• The entry submission system is now open; you can access it here.
• The deadline for the Unofficial Phase has been extended. The new deadline is noon GMT, April 13.

• PhysioNet Challenge data can now be downloaded here.

The 2018 PhysioNet/Computing in Cardiology Challenge is now open. This year's Challenge, "You Snooze, You Win", is focused on the problem of automatically detecting disturbances in a patient's sleep. Given a collection of physiological signals recorded during sleep, including EEG, ECG, and EMG, participants are invited to develop an algorithm to automatically identify arousal events, and will be scored based on how well their algorithm's results agree with those of expert human annotators.

The database contains 32 records of non-contact sleep monitoring by a bioradar. The records are accompanied by results of sleep scoring, based on polysomnography according to the rules of the American Academy of Sleep Medicine.

All the papers from the 2017 Challenge along with their corresponding PDFs are now available.

The Concept Unique Identifier (CUI)-less database contains a corpus of "CUI-less" concepts taken from the SemEval2015 Task 14 that have been assigned CUIs. The annotation process allows assignment of CUIS from any Unified Medical Language System (UMLS) semantic group and compositional normalization using more than one CUI per disease entity. Concepts are mapped to SNOMED CT as represented in the September 2016 version found in Unified Medical Language System (UMLS) 2016AB.

This database contains reference p-wave annotations for twelve signals from the MIT-BIH arrhythmia database.