Challenge Open Access

Noninvasive Fetal ECG - The PhysioNet Computing in Cardiology Challenge 2013

Published: Feb. 21, 2013. Version: 1.0.0


Please include the standard citation for PhysioNet:

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals (2003). Circulation. 101(23):e215-e220.

Introduction

Since the late 19th century, decelerations of fetal heart rate have been known to be associated with fetal distress. Intermittent observations of fetal heart sounds (auscultation) became standard clinical practice by the mid-20th century. The first fetal heart rate (FHR) monitors were developed more than 50 years ago, and became widely available by the mid-1970s. Continuous FHR monitoring was expected to result in dramatic reduction of undiagnosed fetal hypoxia, but disillusionment rapidly set in as studies showed that the outputs of FHR monitors were often unreliable and difficult to interpret, resulting in increased rates of Caesarean deliveries of healthy infants, with little evidence that reductions in adverse outcomes were attributable to the use of FHR monitors (see FHR History).

Improved accuracy in FHR estimation has been achieved through use of more sophisticated signal processing techniques applied to more reliable signals. These improvements, coupled with a better understanding of the limitations of fetal monitoring, have led to wider acceptance. There remains a great deal of room for improvement, however. The most accurate method for measuring FHR is direct fetal electrocardiographic (FECG) monitoring using a fetal scalp electrode. This is possible only in labor, however, and is not common in current clinical practice because of its associated risks.

Noninvasive FECG monitoring makes use of electrodes placed on the mother's abdomen. This method can be used throughout the second half of pregnancy and is of negligible risk, but it is often difficult to detect the fetal QRS complexes in ECG signals obtained in this way, since the maternal ECG is usually of greater amplitude in them.

Figure 1. Four simultaneous noninvasive fetal ECG signals, acquired using electrodes placed on the mother's abdomen, containing both the fetal and the maternal ECGs.. The maternal QRS complexes (not marked) are larger than the fetal QRS complexes (marked in blue). The figure is interactive; click on the large blue circle to remove and restore the marker bars. Click on the Help tab for information about other options.

Other features of the direct fetal ECG, such as FHR variability and fetal QT interval, may be useful independent indicators of fetal status. There are no accepted techniques for assessing such features from noninvasive FECG, however. Before such techniques can be developed, it will be necessary to establish accurate methods for locating QRS complexes and for estimating the QT interval in noninvasive FECG.

The aim of this year's PhysioNet/Computing in Cardiology Challenge is to encourage development of accurate algorithms for locating QRS complexes and estimating the QT interval in noninvasive FECG signals. Using carefully reviewed reference QRS annotations and QT intervals as a gold standard, based on simultaneous direct FECG when possible, the challenge is designed to measure and compare the performance of participants' algorithms objectively. Multiple challenge events are designed to test basic FHR estimation accuracy, as well as accuracy in measurement of inter-beat (RR) and QT intervals needed as a basis for derivation of other FECG features.

Challenge Data Sets

Data for the challenge consist of a collection of one-minute fetal ECG recordings. Each recording includes four noninvasive abdominal signals as illustrated above (figure 1). The data were obtained from multiple sources using a variety of instrumentation with differing frequency response, resolution, and configuration, although in all cases they are presented as 1000 samples per signal per second.

In each case, reference annotations marking the locations of each fetal QRS complex were produced, usually with reference to a direct FECG signal, acquired from a fetal scalp electrode. The direct signals are not included in the challenge data sets, however.

As in recent challenges, the data have been divided into three sets:

  • Learning (training) set A: includes noninvasive fetal ECG signals, as well as the reference annotations for them (for participants' use only; not used to score or rank challenge entries)
  • Open test set B: noninvasive signals only (reference annotations withheld; for evaluation of challenge entries in events 4, 5, and 6)
  • Hidden test set C: unpublished records (reserved for evaluation of open-source challenge entries in events 1, 2, and 3)

Note that the training data set (set A) does not include examples of all of the sources included in the test sets. It will be necessary to design methods for addressing the challenge that are sufficiently flexible to work with data that have recording characteristics that are similar, but not identical, to those in the training set. This feature of the challenge data is intended to encourage participants to develop approaches that are compatible with the variety of devices and signals encountered in clinical practice, and to allow us to assess how successfully participants have been able to accomplish this goal.

Explore training set A and test set B using LightWAVE, our new waveform and annotation viewer. Sets A and B are also available for downloading:

These files are also available as zip archives (set-a.zip, set-a-text.zip, set-a-ext.zip, set-a-ext-text.zip, set-b.zip, and set-b-text.zip). The PhysioBank-compatible files are available individually at /physiobank/database/challenge/2013/set-a and /physiobank/database/challenge/2013/set-b. All five versions contain the same data.

Reference QT interval measurements are in preparation; those for records in set A will be made available to participants when they are complete.

The challenge is to produce a set of annotations and a QT interval measurement that matches the hidden references as nearly as possible, for each record in set B or C.

Reference annotations, heart rates, and RR and QT intervals

Using the direct FECG when possible, reference annotations marking the locations of the fetal QRS complexes (denoted by N) have been derived by crowd-sourcing using a mixture of experts, volunteers, and algorithms.

These reference annotations have been processed to derive the reference RR interval time series (the intervals between successive N annotations).

The reference RR interval time series have been processed further to derive the reference heart rate (HR) time series. Each HR measurement is determined by a widely-used IPFM-based method over a 6-second window (i.e., by the number of reference RR intervals falling within a 6-second interval, including fractional intervals that fall only partly within the interval). Successive windows overlap by 50% (3 seconds), but the first and last windows in each one-minute record are omitted (since the lengths of the initial and final RR interval in the record are unknown), and any windows that include a gap in the RR interval series are likewise omitted. Thus there are at most 16 HR measurements per one-minute record.

A single reference QT interval measurement is produced for each record for which the direct ECG is available. Using the reference QRS annotations to time-align the direct fetal ECG waveforms, the median fetal cardiac cycle is computed, and the reference QT interval is measured manually by inspection of the median cycle. The software used to compute the median cycle is available in source form to participants.

Test annotations, heart rates, RR and QT intervals

Participants create software that annotates a challenge record with reference to the noninvasive ECG signals only. Important: Software created for the challenge should annotate only one record and then exit. In the challenge, it should not be assumed that any other records are available for reference at run-time.

An entry must produce a test annotation file for any record presented to it, in the same format as the reference annotation files. (Either the PhysioBank-compatible binary format, as in the *.fqrs files provided for set A, or the text format, as in the *.fqrs.txt files for set A, is acceptable.) The challenge organizers will derive test RR and HR time series from the test annotation files, using the same software that was used to derive the reference RR and HR time series. This software is available for participants' use in development and self-evaluation of their challenge entries.

Optionally, participants' algorithms may compute a test QT interval measurement for each record, and append that measurement to the test annotation file as shown in the example. It may be advantageous to make such a measurement using a method similar to that used for the reference QT interval measurements, but participants are free to use any method of their choice. Note that QT interval durations will vary among the four noninvasive signals; the challenge is to match (as closely as possible) the reference QT interval measured from the direct signal, which is not available except in the learning set. This will not be easy!

Challenge Events and Scoring

This year's challenge is structured as three pairs of events. Events 1, 2, and 3 are the major events for which monetary awards will be given to the top-ranked participants; they require submission of open-source entries to be tested by the challenge organizers using test set C. Events 4, 5, and 6 are minor events in which the participants submit their entries' annotation files for each record in test set B; non-monetary awards will be given to the top-ranked participants in these events.

Events 1 and 4: Fetal heart rate measurement
In these events, the goal is to produce a set of N annotations that can be used to construct a test FHR time series that closely matches the reference FHR time series, for each recording in the test set. For each reference FHR measurement, a matching test FHR measurement is chosen. If there is no matching FHR measurement, an FHR of zero is used instead.

Scores in these events are computed from the differences between matching reference and test FHR measurements, calculated over the entire test set (C for event 1, B for event 4).

Events 2 and 5: Fetal RR interval measurement
In these events, the goal is to produce a set of N annotations that can be used to construct a test RR interval time series that closely matches the reference RR interval time series, for each recording in the test set. For each reference RR interval, a matching test RR interval is chosen. Matching intervals must begin within 100 milliseconds of each other; if there is no matching test interval, an interval of zero is used instead.

Scores in these events are computed from the differences between matching reference and test RR intervals, calculated over the entire test set (C for event 2, B for event 5).

Event 3: Fetal QT interval measurement
In this event, the goal is to produce an estimate of the median QT interval for each recording in the test set.

Scores in this event are calculated from the differences between matching reference and test QT intervals, calculated over the test set C. Since direct ECG signals, hence reference QT intervals, are not available for all records in the test set, some records will not be scored. Entries should make test QT interval measurements for all records if their authors wish to participate in event 3, since we will not publish a list of records that will be scored for this event.

We originally planned and announced an event 6 for QT interval measurement on test set B. This event has been cancelled. Reliable reference fetal QT measurements are available for only a subset of challenge records, and we planned to score events 3 and 6 using only these records. Since there were fewer such records than anticipated, it was necessary to allocate these records to sets A and C only; hence we are unable to score event 6 since there are no reliable means of determining reference QT intervals for the set B records.

 

Entering the Challenge

To begin, we recommend studying the learning set as preparation for the Challenge itself. A sample entry that can be used as a model for your entries, and software for scoring your entries using the learning set, are available here (in preliminary versions, physionet2013.m and genresults.m, for MATLAB). Scores obtained from learning set data are not used for ranking entries, however!  We provide the scoring software so that participants can verify that they are able to prepare properly formatted entries. In general, participants should expect that official scores obtained using the test sets will differ from unofficial scores obtained using the learning set, especially if the algorithms have been (over)trained.

All entries must include (or be able to produce) annotation files in the format of the reference annotation files provided for set A.

Open-source entries must include the sources for the software used to produce the annotations. As in the sample entries, your entry must write the annotations to standard output rather than to a file; our test framework captures the standard output of your entry for processing. Your entry may be written in portable (ANSI/ISO) C or MATLAB/Octave m-code; other languages, such as C++, Java, Perl, Python, and R, may be acceptable, and your entry may make use of open-source libraries that are available for Linux, but please ask us first, and do so no later than 7 April 2013.

Participants submitting open-source entries are entered into events 1 and 2, and into event 3 if their entries include QT measurements. These entries will be run on set C by the challenge organizers; scores will be returned as soon as possible after submission, but participants should expect that scoring will require a day or two (possibly longer near the major deadlines).

Participants submitting set B annotations ("closed-source entries") are entered into events 4 and 5, and into event 6 if their entries include QT measurements.

Participants may submit both open-source entries and closed-source entries, but the total number of entries from any participant or team is limited to 8 (3 in Phase 1, and 5 in Phase 2, as described below).

Awards will be presented to the most successful eligible participants during Computing in Cardiology (CinC) 2013. To be eligible for an award, you must:  

  1. Join PhysioNetWorks if you are not already a member, and follow the link from your PhysioNetWorks home page to "PhysioNet/CinC Challenge 2013" to register as a particpant. Joining the project creates a Challenge Participant Page for you, where you will submit your entries and receive your scores.
  2. Submit a preliminary Challenge entry via PhysioNetWorks no later than 25 April 2013. (The period before this deadline is Phase 1.) You may submit up to three Phase 1 entries before this deadline, at most one entry per week. (Use them or lose them!)
  3. Submit an acceptable abstract on your work on the Challenge to Computing in Cardiology no later than 1 May 2013. Include an event 1 test set score (and optionally an event 2 test set score) for at least one Phase 1 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.
  4. Submit a final Challenge entry via PhysioNetWorks during Phase 2 (on or after 1 June but no later than 25 August 2013). You may submit up to five Phase 2 entries between 1 June and 25 August, at most one entry per week.
  5. The test set scores (from either Phase 1 or Phase 2) will determine the final rankings of the entries, with the top-ranked entries in each event eligible for awards.
  6. Submit a full (4-page) paper on your work on the Challenge to CinC no later than 9 September 2013.
  7. Attend CinC 2013 (22-25 September 2013, in Zaragoza, Spain) and present your work there.

An important goal of this Challenge, and of others in the annual series of PhysioNet/CinC Challenges, is to accelerate progress on the Challenge questions, not only during the limited period of the Challenge, but also afterward. In pursuit of this goal, we strongly encourage participants to submit open-source entries that will be made freely available after the conclusion of the Challenge via PhysioNet.

Eligible authors of the entries that receive the best test set scores in each Challenge event will receive award certificates during the closing plenary session of CinC on 25 September 2013. In recognition of their contributions to further work on the Challenge problems, eligible authors of the open-source entries that receive the best test set scores will also receive monetary awards. No team or individual will receive more than one such monetary award.

Frequently asked questions about the Challenge

If I don't submit all 3 preliminary (Phase 1) entries, can I add the unused ones to my quota of 5 Phase 2 entries?

No. We are trying to encourage both experimentation with multiple approaches and sustained effort. In past Challenges some participants have used their entire allowance of entries before the first deadline, and others have saved their entries until hours before the final deadline. The most successful participants have usually reflected on each set of results, refining their ideas (and not merely their decision thresholds) before submitting the next entry. This approach yields better results, and it also allows us to review your entries and provide scores more rapidly than if we receive a large fraction of them just before the deadlines.

Should annotations be written to files? The rules are not clear.

The output for each 1-minute test record should be a file of annotations.

If you enter the open-source events (1, 2, and 3), your entry must be in the form of software. We will run your software on each of the records in the hidden test set (set C), and redirect its standard output to a file for each test record. The requirement to write to standard output rather than to a named file allows us to run your software in a secure "sandbox" environment, which is necessary to make it practical for us to conduct these events.

If you enter the other events (4, 5, and 6), your entry should be a set of annotation files that you will create yourself by running your software using each of the records in the open test set (set B). Since you will run your software yourself for these events, it may create the files directly, or (as for the open-source events) it can write the annotations to its standard output and you can capture that standard output in a file.

See What is a "standard input" or a "standard output"? if this is confusing.

An additional point of potential confusion is that we have provided reference annotation files for the training set (set A) in both .fqrs (PhysioBank- compatible binary) and .fqrs.txt (text) formats. We expect that most participants will find it easier to generate text-format output, but we will accept either format.

Do the numbers in the .fqrs files refer to the line numbers in the MS Excel files, or do they refer to the time values in column A of the MS Excel files?

There are no MS Excel files in the challenge data sets.

The .fqrs.txt files contain times of occurrence of fetal QRS complexes in the respective records. Each number is the elapsed time in milliseconds from the beginning of the record to a fetal QRS. The .csv files contain five columns; the first two lines describe the contents of these columns and the units. Beginning on the third line, the first column contains the elapsed time in seconds from the beginning of the record to the time of observation of the samples of the four signals in the remaining columns. So, for example, the first number in a01.fqrs.txt is 355, and this marks a fetal QRS that occurs 355 milliseconds after the beginning of record a01. The corresponding samples are those in the line of a0.csv that begins with 0.355 (which is the 358th line in a0.csv, since the first two lines are column headers and the third line begins with 0.000).

The .fqrs files contain the same information as the .fqrs.txt files, but in the binary format that is used throughout PhysioBank for annotation files. It is readable using software in the WFDB software package, but not using MS Excel. Similarly, the .dat files contain the same information as the .csv files, but in a binary PhysioBank- and WFDB-compatible format that cannot be read using MS Excel.

What does '-' mean in the .csv files?

What does '-32768' mean in the .dat files?

The special value '-' appears in the .csv files, and the special value -32768 appears in the .dat files, when the output of the A/D converter is invalid (for example, if the analog signal is out of the input range of the ADC, or if the transducer or cables are disconnected). Samples with this special value indicate that no valid observation of the signal was recorded during that sampling interval; you may either ignore them or use the information that the signal was lost as a contribution to an assessment of the quality of the signal.

Why are there separate events for fetal HR (1 and 4) and fetal RR (2 and 5)? How could there be any difference in the outcomes of these events?

It's likely that rankings for events 1 and 2 (or 4 and 5) will be correlated, but it is quite possible to obtain good results in event 1 and poor results in event 2 or vice versa.

An entry that detects fQRSs but does not determine their locations accurately will score well on event 1 and poorly on event 2. This might happen if the numbers of false negatives (missed fQRSs) and false positives (extra detections) are nearly equal, as would be expected of an optimized fQRS detector in the presence of significant noise; the FHR would be fairly accurate but the FRR intervals would not be.

An entry that includes a high-specificity fQRS detector may make many more false negative errors than false positive errors in the presence of significant noise; it will underestimate FHR and thus will score poorly on event 1, but if it is able to determine accurate locations for consecutive true positive detections, it will score well on event 2.

For entries in the open-source events, must I submit the source code, or just a statement of what software was used?

Participants in the open-source events (1, 2, and 3) must supply the sources (i.e., the actual source code, not just a reference to it) for their entries. We will publish a selection of these entries on PhysioNet after the conclusion of the challenge, with full credit to their authors.

May I submit an open-source entry (for events 1, 2, and 3) that includes functions that will not be published?

Sorry, no. The open-source events require complete open-source entries, because our aim is to advance the state of the art by encouraging the development and publication of software that others can use, adapt to their needs, and develop further if they wish.

Participants are free to make use of open-source implementations of functions in open-source challenge entries. Open-source implementations of common functions such as cross-correlation are easy to find. Open-source QRS detection functions (suitable for finding the maternal QRS complexes) are also available (for example, see gqrs, sqrs, or wqrs in the WFDB software package).

Each participant-team may submit up to 10 entries, and they can be any mixture of open-source and non-open-source entries.

How can I score my entry in MATLAB or Windows?

Note: before running this new tool please make sure that the directory in which you are running it on has been properly and fully backed up. The scoring procedure requires a JAR file, that can be obtained by downloading and installing the WFDB App Toolbox for MATLAB.

 

The scoring requires that all the data and annotations for one set be stored in the same directory. For instance, in scoring record a01, the following files are necessary at the testing directory:

 

  • a01.dat - WFDB Binary Data File (provided by PhysioNet)
  • a01.hea - WFDB Header File (provided by PhysioNet)
  • a01.fqrs - WFDB Binary Annotation File (provided by PhysioNet)
  • a01.entry1 - WFDB Binary Annotation File (generated by the user)

NON-MATLAB Users can score their entries by running the following command from a terminal or command prompt (assuming the WFDB App toobox was installed at /home/foo/wfdb and the annotation data reside in /home/foo/pn2013/data):

           java -cp "/home/foo/wfdb/mcode/wfdb-app-JVM6-0-0-2.jar" org.physionet.wfdb.Score2013 a01 /home/foo/pn2013/data/ fqrs entry1

This will score your entry using Java's virtual machine.

MATLAB Users can score their individual entries by running the following command from the MATLAB prompt (assuming same installation as the example above ):

   
          cd /home/foo/pn2013/data;
          [score1,score1]=score2013('a01','fqrs','entry1')

Alternatively, MATLAB users can also run the genresults.m script that scores all the files in that directory (writing the output of physionet2013.m into WFDB annotation binaries files).

Note for Windows users: The scoring procedure requires Administrator privileges in order to generate some temporary cache files. To run the scoring procedure in MATLAB or at the command prompt, before you start the MATLAB or command prompt make sure you right click on it and select the "run as Administrator" option. The source code for the patchtann.c, the JAR file, and the toolbox can be found here and you can compile it from source if you wish.

Acknowledgments

The challenge organizers wish to thank Joachim Behar, Gari Clifford, Marcelino Martinez, Dawid Roj, Reza Sameni, Anton Tokarev, and their colleagues for their generous contributions of data and expertise in support of this challenge.

References

[FHR history]:
Jenkins HML. Thirty years of electronic intrapartum fetal heart rate monitoring: discussion paper. J R Soc Med 1989 Apr; 82(4):210-214.

Kennedy RG. Electronic fetal heart rate monitoring: retrospective reflections on a twentieth-century technology. J R Soc Med 1998 May; 91(5): 244–250.

[FECG]:
Neilson JP. Fetal electrocardiogram (ECG) for fetal monitoring during labour. Cochrane Database Syst Rev. 2012 Apr 18;4:CD000116

Sameni R, Clifford GD. A review of fetal ECG signal processing; issues and promising directions. Open Pacing Electrophysiol Ther J. 2010 Jan; 3:4–20.

[Fetal QT interval]:
Oudijk MA, Kwee A, Visser GH, Blad S, Meijboom EJ, Rosén KG. The effects of intrapartum hypoxia on the fetal QT interval. BJOG 2004 Jul; 111(7)656-660.
[IPFM]:
Berger RD, Akselrod S, Gordon D, Cohen RJ. An efficient algorithm for spectral analysis of heart rate variability. IEEE TBME 1986;9:900-904.

 Challenge Results

Over 60 teams participated in the 2013 Challenge on noninvasive fetal ECG. Below are the scores of the top-ranked teams. (Only 6 teams participated in event 3, and only 2 produced entries that could be scored.) Each team was allowed to submit up to 11 entries; the scores shown below are the best (lowest) received by each team for any of their entries.

Event 1 (Fetal heart rate estimation, evaluated using hidden test set C)

The score for each record was the mean squared error (in bpm2) between the fetal heart rate signals estimated from the reference and test annotations. The aggregate score was the mean of the scores for each record.

Participant Score
Maurizio Varanini, Gennaro Tartarisco, Lucia Billeci, Alberto Macerata, Giovanni Pioggia and Rita Balocchi 187.091
Piotr Podziemski and Jan Gierałtowski 255.989
Rui Rodrigues 278.755
Luigi Yuri Di Marco, Alberto Marzo and Alejandro Frangi 380.853
Jakub Kuzilek and Lenka Lhotska 492.412
Joseph McBride, Brent McFerrin, Craig Towers and Xiaopeng Zhao (unofficial) 505.634
Aruna Deogire and Satish Hamde (unofficial) 580.284
Alessia Dessì, Danilo Pani and Luigi Raffo 684.158
Tian Wenlong (unofficial) 931.156
Joachim Behar, Julien Oster and Gari Clifford (unofficial) (179.439)

Event 2 (Fetal RR interval estimation, evaluated using hidden test set C)

The score for each record was the root mean squared difference (in ms) between corresponding reference and test RR intervals. The aggregate score was the mean of the scores for each record.

Participant Score
Maurizio Varanini, Gennaro Tartarisco, Lucia Billeci, Alberto Macerata, Giovanni Pioggia and Rita Balocchi 20.975
Piotr Podziemski and Jan Gierałtowski 25.059
Luigi Yuri Di Marco, Alberto Marzo and Alejandro Frangi 26.085
Rui Rodrigues 28.201
Joseph McBride, Brent McFerrin, Craig Towers and Xiaopeng Zhao (unofficial) 29.680
Jakub Kuzilek and Lenka Lhotska 33.200
Akshay Dhawan (unofficial) 39.667
Andrea Fanelli, Giovanni Magenes and Maria Gabriella Signorini (unofficial) 42.859
Alessia Dessì, Danilo Pani and Luigi Raffo 47.990
Joachim Behar, Julien Oster and Gari Clifford (unofficial) (20.793)

Event 3 (Fetal QT interval estimation, evaluated using hidden test set C)

For each record, the median FQT interval was estimated. The aggregate score was the root mean squared difference (in ms) between the reference and test FQT intervals for all records.

Participant Score
Piotr Podziemski and Jan Gierałtowski 152.71
Joachim Behar, Julien Oster and Gari Clifford (unofficial) (153.07)

Event 4 (Fetal heart rate estimation, evaluated using open test set B)

The score for each record was the mean squared error (in bpm2) between the fetal heart rate signals estimated from the reference and test annotations. The aggregate score was the mean of the scores for each record.

Participant Score
Fernando Andreotti, Maik Riedl, Tilo Himmelsbach, Daniel Wedekind, Sebastian Zaunseder, Niels Wessel and Hagen Malberg 18.803
Jukka A Lipponen and Mika P Tarvainen 28.893
Maurizio Varanini, Gennaro Tartarisco, Lucia Billeci, Alberto Macerata, Giovanni Pioggia and Rita Balocchi 33.952
Masoumeh Haghpanahi and David A Borkholder 50.063
Minnan Xu-Wilson, Eric Carlson, Limei Cheng and Srinivasan Vairavan 52.496
Ali Ghaffari, Seyyed Abbas Atyabi, Mohammad Javad Mollakazemi, Maryam Niknami and Ali Soleiman 63.750
Mantas Lukoševičius and Vaidotas Marozas 66.327
Martin Kropf, Günter Schreier, Robert Modre-Osprian and Dieter Hayn 82.438
   
Joachim Behar, Julien Oster and Gari Clifford (unofficial) (29.619)

Event 5 (Fetal RR interval estimation, evaluated using open test set B)

The score for each record was the root mean squared difference (in ms) between corresponding reference and test RR intervals. The aggregate score was the mean of the scores for each record.

Participant Score
Fernando Andreotti, Maik Riedl, Tilo Himmelsbach, Daniel Wedekind, Sebastian Zaunseder, Niels Wessel and Hagen Malberg 4.337
Jukka A Lipponen and Mika P Tarvainen 4.844
Maurizio Varanini, Gennaro Tartarisco, Lucia Billeci, Alberto Macerata, Giovanni Pioggia and Rita Balocchi 5.098
Martin Kropf, Günter Schreier, Robert Modre-Osprian and Dieter Hayn 7.354
Mantas Lukoševičius and Vaidotas Marozas 8.239
Masoumeh Haghpanahi and David A Borkholder 9.062
Christoph Maier and Hartmut Dickhaus 9.326
Minnan Xu-Wilson, Eric Carlson, Limei Cheng and Srinivasan Vairavan 10.618
Vito Starc 10.920
Joachim Behar, Julien Oster and Gari Clifford (unofficial) (4.672)

How the entries were scored

Entries were scored using tach, mxm, ann2rr, and custom software developed for the challenge. For each event, the software processed the reference annotations and the test annotations (i.e., those generated by the participant’s entry) to obtain a score for each test record, then calculated an average score for all records in the test set.

Participants had access to training set A (both signals and reference annotations) and open test set B (signals only); these data sets remain freely available. Reference annotations for set B, and both the signals and annotations for hidden test set C, will be kept hidden, to allow fair comparisons between entries submitted during the challenge and those developed in followup studies by challenge participants as well as other interested researchers.

Note on unofficial participants

Teams listed above as "unofficial" participated in the Challenge and achieved excellent results.

Entries from the team of Joachim Behar, Julien Oster, and Gari Clifford were unofficial since their authors were among the challenge organizers, and had major roles in developing the challenge questions and data sets.

Other teams listed as unofficial were unable to attend CinC 2013 and discuss their work, a condition of eligibility for Challenge awards.

Papers

The papers below were presented at Computers in Cardiology 2013. Please cite this publication when referencing any of these papers. These papers have been made available by their authors under the terms of the Creative Commons Attribution License 3.0 (CCAL). We wish to thank all of the authors for their contributions.

The first of these papers is an introduction to the challenge topic, with a summary of the challenge results and a discussion of their implications.

Noninvasive Fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013
Ikaro Silva, Joachim Behar, Reza Sameni, Tingting Zhu, Julien Oster, Gari D Clifford, George B Moody

The remaining papers were presented by participants in the Challenge, who describe their approaches to the challenge problem.

Cancellation of the Maternal and Extraction of the Fetal ECG in Noninvasive Recordings
Ivaylo Christov, Iana Simova, Roger Abächerli

Extracting the R-Wave Position from an FECG Record usingRecognition of Multi-channel Shapes
Filip Plešinger, Pavel Jurák, Josef Halámek

Advanced Maternal ECG Removal and Noise Reduction for Application of Fetal QRS Detection
Jukka A Lipponen, Mika P Tarvainen

Fetal QRS Detection and RR Interval Measurement in Noninvasively Registered Abdominal ECGs
Christoph Maier, Hartmut Dickhaus

Noninvasive Fetal QRS Detection using a Linear Combination of Abdomen ECG Signals
Or Perlman, Amos Katz, Yaniv Zigel

Fetal ECG Extraction From Abdominal Recordings usingArray Signal Processing
Masoumeh Haghpanahi, David A Borkholder

Advanced Signal Processing Techniques for Fetal ECG Analysis
Jakub Kuzilek, Lenka Lhotska

Fetal QRS Complex Detection using Semi-Blind Source Separation Framework
Fatemeh Razavipour, Masoumeh Haghpanahi, Reza Sameni

Fetal QRS Complex Detection Based on Three-Way Tensor Decomposition
Mohammad Niknazar, Bertrand Rivet, Christian Jutten

Fetal Electrocardiogram R-peak Detection using Robust Tensor Decomposition and Extended Kalman Filtering
Mahsa Akhbari, Mohammad Niknazar, Christian Jutten, Mohammad B Shamsollahi, Bertrand Rivet

Maternal Signal Estimation by Kalman Filtering and Template Adaptation for Fetal Heart Rate Extraction
Fernando Andreotti, Maik Riedl, Tilo Himmelsbach, Daniel Wedekind, Sebastian Zaunseder, Niels Wessel, Hagen Malberg

Spatial Filtering and Adaptive Rule Based Fetal Heart Rate Extraction from Abdominal Fetal ECG Recordings
Minnan Xu-Wilson, Eric Carlson, Limei Cheng, Srinivasan Vairavan

A Robust Framework for Noninvasive Extraction of Fetal Electrocardiogram Signals
Marzieh Fatemi, Mohammad Niknazar, Reza Sameni

Noninvasive Fetal QRS Detection Using Echo State Network
Mantas Lukoševičius, Vaidotas Marozas

A Multi-step Approach for Non-invasive Fetal ECG Analysis
Maurizio Varanini, Gennaro Tartarisco, Lucia Billeci, Alberto Macerata, Giovanni Pioggia, Rita Balocchi

Noninvasive Fetal ECG Estimation Based on Linear Transformations
Mariano Llamedo, Alba Martín-Yebra, Pablo Laguna, Juan Pablo Martínez

A Wavelet-Based Method for Assessing Fetal Cardiac Rhythms from Abdominal ECGs
Rute Almeida, Hernâni Gonçalves, Ana Paula Rocha, João Bernardes

PhysioNet/CinC Challenge 2013: A Novel Noninvasive Technique to Recognize the Fetal QRS Complexes from Noninvasive Fetal Electrocardiogram Signals
Ali Ghaffari, Seyyed Abbas Atyabi, Mohammad Javad Mollakazemi, Mohammad Niknazar, Maryam Niknami, Ali Soleimani

Non Invasive FECG Extraction from a Set of Abdominal Sensors
Joachim Behar, Julien Oster, Gari D Clifford

Multi Stage Principal Component Analysis Based Method for Detection of Fetal Heart Beats in Abdominal ECGs
Robertas Petrolis, Algimantas Krisciukaitis

An Algorithm for the Analysis of Foetal ECGs from 4-channel Non-invasive Abdominal Recordings
Costanzo Di Maria, Wenfeng Duan, Marjan Bojarnejad, Fan Pan, Susan King, Dingchang Zheng, Alan Murray, Philip Langley

Systematic Methods for Fetal Electrocardiographic Analysis: Determing the Fetal Heart Rate, RR Interval and QT Interval
Chengyu Liu, Peng Li

A Robust Algorithm for Fetal QRS Detection using the Non-invasive Maternal Abdomen ECGs
Martin Kropf, Robert Modre-Osprian, Günter Schreier, Dieter Hayn

Non-invasive Fetal Multilead RR Interval Determination from Maternal Abdominal Recordings: the Physionet/CINC Challenge 2013
Vito Starc

Identification of Fetal QRS Complexes in Low Density Non-Invasive Biopotential Recordings
Alessia Dessì, Danilo Pani, Luigi Raffo

Fetal ECG Detection in Abdominal Recordings: a Method for QRS Location
Rui Rodrigues

Multichannel Foetal Heartbeat Detection by Combining Source Cancellation with Expectation-weighted Estimationof Fiducial Points
Luigi Yuri Di Marco, Alberto Marzo, Alejandro Frangi

Fetal Heart Rate Discovery: Algorithm for Detection of Fetal Heart Rate from Noisy, Noninvasive Fetal ECG Recordings
Piotr Podziemski, Jan Gierałtowski


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