Challenge Open Access
Reducing False Arrhythmia Alarms in the ICU - The PhysioNet Computing in Cardiology Challenge 2015
Published: Feb. 15, 2015. Version: 1.0.0
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.
The 2015 PhysioNet/CinC Challenge aims to encourage the development of algorithms to reduce the incidence of false alarms in the Intensive Care Unit (ICU). False alarms in the ICU can lead to a disruption of care, impacting both the patient and the clinical staff through noise disturbances, desensitization to warnings and slowing of response times , leading to decreased quality of care [2,3]. ICU alarms produce sound intensities above 80 dB that can lead to sleep deprivation [1,4,5], inferior sleep structure [6,7], stress for both patients and staff [10,11,12,13] and depressed immune systems . There are also indications that the incidence of re-hospitalization is lower if disruptive noise levels are decreased during a patient's stay . Furthermore, such disruptions have been shown to have an important effect on recovery and length of stay [2,10]. In particular, cortisol levels have been shown to be elevated (reflecting increased stress) [12,13], and sleep disruption has been shown to lead to longer stays in the ICU . ICU false alarm (FA) rates as high as 86% have been reported, with between 6% and 40% of ICU alarms having been shown to be true but clinically insignificant (requiring no immediate action) . In fact, only 2% to 9% of alarms have been found to be important for patient management .
In this challenge we focus only on life threatening arrhythmias, namely asystole, extreme bradycardia, extreme tachycardia, ventricular tachycardia, and ventricular flutter/fibrillation (Table 1). Entrants are encouraged to review previous and related work on this topic [18-27] and to identify key issues that have arisen in this topic in the past.
|Asystole||No QRS for at least 4 seconds|
|Extreme Bradycardia||Heart rate lower than 40 bpm for 5 consecutive beats|
|Extreme Tachycardia||Heart rate higher than 140 bpm for 17 consecutive beats|
|Ventricular Tachycardia||5 or more ventricular beats with heart rate higher than 100 bpm|
|Ventricular Flutter/Fibrillation||Fibrillatory, flutter, or oscillatory waveform for at least 4 seconds|
- Download the training set: training.zip and the sample MATLAB entry: entry.zip.
- Create a free PhysioNetWorks account and join the PhysioNet/CinC Challenge 2015 project.
- Develop your entry by making the following edits to entry.zip:
- Modify the sample entry source code file challenge.m with your changes and improvements. For additional information, see the Preparing an Entry for the Challenge section.
- Modify the AUTHORS.txt file to include the names of all the team members.
- Unzip training.zip and move all its files to the top directory of your entry directory (where challenge.m is located.)
- Run your modified source code file on all the records in the training set by executing the script generateValidationSet.m. This will also build a new version of entry.zip.
- Optional: Include a file named DRYRUN at the top directory of your entry (where the AUTHORS.txt file is located) if you do not wish your entry to be scored and counted against your limit. This is useful in cases where you wish to make sure that the changes made do not result in any error.
- Submit your modified entry.zip for scoring through the PhysioNetWorks PhysioNet/CinC Challenge 2015 project. The contents of entry.zip must be laid out exactly as in the sample entry. Improperly-formatted entries will not be scored.
For those wishing to compete officially, please follow the additional four steps described in the Rules and Deadlines.
Join our community Community Discussion Forum to get the latest challenge news, technical help, or if you would like to find partners to collaborate with.
Rules and deadlines
Participants may compete in multiple events:
Event 1 The goal is to reduce the maximum number of false alarms, while avoiding the suppression of true alarms in "real-time" (i.e. using no information after the sounding of the alarm).
Event 2 The goal is to reduce the maximum number of false alarms, while avoiding the suppression of true alarms "retrospectively", using up to 30 seconds of data after the alarm.
Entrants may compete in one or both events, and may have an overall total of up to 15 submitted entries (you can submit entries that run simultaneously on both events). Each participant may receive scores for up to five entries submitted during the unofficial phase and ten entries at the end of the official phase. Unused entries may not be carried over to later phases. Entries that cannot be scored (because of missing components, improper formatting, or excessive run time) are not counted against the entry limits.
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!
noon GMT on
noon GMT on
|Unofficial Phase||18 February||5||10 April|
|[Hiatus]||10 April||0||16 April|
|Official Phase||16 April||10||21 August|
All official entries must be received no later than the noon GMT on Friday, 21 August 2015. 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:
- Submit at least one open-source entry that can be scored before the Phase I deadline (noon GMT on Friday, 10 April 2015).
- Submit an acceptable abstract (about 300 words) on your work on the Challenge to Computing in Cardiology no later than 14 April 2015. Include the overall score for at least one 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.
- Submit a full (4-page) paper on your work on the Challenge to CinC no later than 1 September 2015.
- Attend CinC 2015 (7-10 September 2015) and present your work there.
Please do not submit analysis of this year's Challenge data to other conferences or journals until after CinC 2015 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 2015) to submit extended analyses and articles to that issue, taking into account the publications and discussions at CinC 2015.
Bedside monitor data leading up to a total of 1250 life-threatening arrhythmia alarms recorded from three of the largest intensive care monitor manufacturers' bedside units will be used in this challenge. Data are sourced from four hospitals in the USA and Europe, chosen at random (and so do not necessarily represent the true statistics for false alarm rates for any given manufacturer or hospital which is likely to be different based on unit-specific protocols, software versions and unit types). The training and test sets have each been divided into two subsets of mutually exclusive patient populations. The training set contains 750 recordings and the test set contains 500 recordings. The test is unavailable to the public and will remain private for the purpose of scoring. No more than three alarms of each of the five categories are used from any given patient, and alarms are at least 5 minutes apart (usually longer). In this way, the competition does not address the issue of what to do with repeated alarms and how to use information from earlier alarms. Although this could be done with the full files from each patient, this may also a dangerous practice, because any algorithm would propagate and compound any errors from one alarm to the next. A team of expert annotators reviewed each alarm and labeled it either 'true', 'false', or 'impossible to tell'. The Challenge includes only records that were reviewed by at least two annotators, of whom a two-thirds majority agreed that the alarm was either true or false.
An alarm was triggered 5 minutes from the beginning of each record. The exact time of the event that triggered the alarm varies somewhat from one record to another, but in order to meet the ANSI/AAMI EC13 Cardiac Monitor Standards , the onset of the event must be within 10 seconds of the alarm (i.e., between 4:50 and 5:00 of the record). Note that there may have been additional arrhythmia events in the 5 minutes preceding the alarm; these events have not been annotated.
In the "real-time" subset, each record is exactly five minutes long, so your program does not have any information beyond what was known to the monitor at the time the alarm was triggered. In the "retrospective" subset (50% of the training set), each record contains an additional 30 seconds of data following the time of the alarm. All entries are assumed to be competing in the "real-time" category only, information on how to change your entry to compete in the "retrospective" category, or both, please see the Preparing an entry for the challenge section. If you choose to compete in both categories, your submitted entry will receive a two scores ( one for each category ). We invite competitors to try to make use of the additional information provided by the retrospective records.
All signals have been resampled (using anti-alias filters) to 12 bit, 250 Hz and have had FIR band pass [0.05 to 40Hz] and mains notch filters applied to remove noise. Pacemaker and other nose artifacts may be present on the ECG. Pulsatile channels can suffer from movement artifact, sensor disconnects and other events (such as line flushes or coagulation in the catheter ). Each recording contains two ECG leads (which may or may not be the leads that triggered the alarm) and one or more pulsatile waveforms (the photoplethysmogram and/or arterial blood pressure waveform).
We have chosen not to provide the beat markers that the bedside alarm algorithms may have used to trigger the alarm. We also note that tachycardia and bradycardia alarms have variable thresholds, which are sometimes adjusted at the bedside. We have also removed these for consistency.
The Challenge data are provided in WFDB format with an Octave/MATLAB-compatible header. You can load the data directly using MATLAB or Octave without any special tools, but you must take care to scale it into the correct physical units. You can also use the WFDB MATLAB Toolbox function RDMAT ; to load data in physical units; see below.
As a starting point we have provided a fairly simple algorithm (entry.zip): one which checks the quality of the blood pressure waveform, and (if it is acceptable) uses it to calculate the heart rate. If no blood pressure is available then the same process is performed on the photoplethysmogram (PPG, or pulse oximetry waveform). If the heart rate derived from the blood pressure (or PPG) is outside the (variable) range indicated by the alarm, then the alarm is suppressed. In the case of asystole, we expect our morphology based quality algorithms to report 'low quality' on all pulse channels because no strong regular pulse is present. Therefore the asystole alarm is suppressed only if the BP or PPG quality metrics we use report a high quality value. Note that other signal quality algorithms may not respond in the same manner.
You may want to begin with this framework, and add more intelligent approaches, or discard it completely and start from scratch. We do not provide it because it is necessarily a good approach, but simply because it is an obvious one, which uses open source software and previously described techniques. It is particularly unsuitable for VT and VF.
NOTE: You do not need additional software beyond MATLAB to run the sample entry or enter the competition. The sample entry has not been tested in Octave. The sample entry uses one file access function, RDMAT, which reads the Challenge's binary data and scales it appropriately. Although this function is part of the WFDB Toolbox for MATLAB, it has no dependencies and its most up-to-date version can be found at the WFDB Toolbox repository.
Preparing an entry for the challenge
To participate in the challenge, you will need to create software that is able to read the test data and output the final alarm result without user interaction in our test environment. Two sample entries (entry.zip, written in MATLAB, and entry.tar.gz, written in Perl) are available to help you get started. In addition to MATLAB, you may use any programming language (or combination of languages) supported using open source compilers or interpreters on GNU/Linux, including C, C++, Fortran, Haskell, Java, Octave, Perl, Python, and R.
If your entry requires software that is not installed in our sandbox environment, please let us know before the end of Phase I. We will not modify the test environment after the start of Phase II of the challenge.
- setup.sh, a bash script run once before any other code from the entry; use this to compile your code as needed
- next.sh, a bash script run once per training or test record; it should analyze the record using your code, saving the results as a text file for each record.
- answers.txt, a text file containing the results of running your program on each record in the training set. These results are used for validation only, not for ranking entries (see below).
- AUTHORS.txt, a plain text file listing the members of your team who contributed to your code, and their affiliations
- LICENSE.txt, a text file containing the license for your software (the default is the GPL). All entries are assumed to be open source and will eventually be released on PhysioNet (for closed source entries please see below).
See the comments in the sample entry's setup.sh and next.sh if you wish to learn how to customize these scripts for your entry.
We verify that your code is working as you intended, by comparing the answers.txt file that you submit with your entry, with answers produced by your code running in our test environment using the same records. If your code passes this validation test, it is then evaluated and scored using the hidden test data set. The scores in the hidden data set determines the ranking of the entries and the final outcome of the Challenge.
In addition to the required components, your entry may include a file named DRYRUN. If this file is present, your entry is not evaluated using the hidden test data, and it will not be counted against your limit of entries per phase; you will receive either a confirmation of success or a diagnostic report, but no scores. Use this feature to verify that none of the required components are missing, that your setup.sh script works in the test environment, and that your next.sh script produces the expected output for the training data within the time limits.
Closed Source Entries
Although the competition is only for open source entries, we also accept the submission of closed-source entries from industry or from individuals. If you enter closed source, we will not publish your code or score (unless you specifically request that we do so). However, the default entry is open source (GPL), so you must explicitly indicate that your entry is closed source by including with your entry a file called CLOSEDSOURCE.txt and modifying LICENSE.txt accordingly. If you submit an executable, it must be compiled to run in our testing environment (Ubuntu 14.04 amd64.)
Open source entry scores will not be posted until after the close of the Unofficial Phase, and closed source entries will not be posted. You may choose to swap between being open source or closed source at any time up to the end of the Unofficial Phase by inserting or removing the CLOSEDSOURCE.txt file with your final entry prior to the end of the Unofficial Phase.
If your entry is properly formatted, and nothing is missing, it is tested and scored automatically, and you will receive your scores when the test is complete (depending on your entry's run time, this may take an hour or more). If you receive an error message instead, read it carefully and correct the problem(s) before resubmitting.
The scoring for this challenge will be a function of the following variables: true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). Where these variables are defined as below:
|True Alarm||False Alarm|
More specifically, competitors should attempt to maximize TP and TN while minimizing FP and FN. The scoring will weight FN more heavily than the FP. The script score2015Challenge.m in the sample entry, entry.zip, generates these statistics on the training set for MATLAB based entries, but does not give you a final score. The final scoring equation was not included with the sample entries because was under testing during the initial stage of the challenge. If you wish to calcualte the final score for your entry, you can do so by implementing the equation :
|Score = ( TP + TN ) / ( TP + TN + FP + 5*FN )|
Obtaining complimentary MATLAB licenses
The MathWorks has kindly decided to sponsor Physionet's 2015 Challenge. 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 Mathwork's PhysioNet Challenge link. If you have questions or need technical support, please contact The MathWorks at firstname.lastname@example.org.
 Chambrin MC. Review: Alarms in the intensive care unit: how can the number of false alarms be reduced? Critical Care. 2001 Aug; 5(4):184-8. Epub 2001 May 23.
 Donchin Y, Seagull FJ. The hostile environment of the intensive care unit. Curr Opin Crit Care. 2002 Aug;8(4):316-20.
 Imhoff M, Kuhls S. Alarm algorithms in critical care monitoring. Anesth Analg., 2006 May; 102(5):1525-37.
 Meyer TJ, Eveloff SE, Bauer MS, Schwartz WA, Hill NS, Millman RP. Adverse environmental conditions in the respiratory and medical ICU settings. Chest. 1994 Apr; 105(4), 1211-16.
 Parthasarathy S, Tobin MJ. Sleep in the intensive care unit. Intensive Care Med. 2004 Feb; 30(2), 197-206.
 Johnson AN. Neonatal response to control of noise inside the incubator. Pediatr Nurs. 2001 Nov-Dec; 27(6), 600-5.
 Slevin M, Farrington N, Duffy G, Daly L, Murphy JF. Altering the NICU and measuring infants' responses. Acta Paediatr. 2000 May; 89(5), 577-81.
 A.J. Cropp, L.A. Woods, D. Raney, D.L. Bredle, Name that tone. The proliferation of alarms in the intensive care unit. Chest, 105 (4) (1994), pp. 1217-1220 Apr
 M.A. Novaes, A. Aronovich, M.B. Ferraz, E. Knobel, Stressors in ICU: patients' evaluation, Intens Care Med, 23 (12) (1997), pp. 1282-1285 Dec
 M. Topf, S. Thompson, Interactive relationships between hospital patients' noise induced stress and other stress with sleep, Heart Lung, 30 (4) (2001), pp. 237-243 Jul-Aug
 W.E. Morrison, E.C. Haas, D.H. Shaffner, E.S. Garrett, J.C. Fackler, Noise, stress, and annoyance in a pediatric intensive care unit, Crit Care Med, 31 (1) (2003), pp. 113-119 Jan
 S. Berg, Impact of reduced reverberation time on sound-induced arousals during sleep, Sleep, 24 (3) (2001), pp. 289-292 May 1
 I. Hagerman, G. Rasmanis, V. Blomkvist, R. Ulrich, C.A. Eriksen, T. Theorell, Influence of intensive coronary care acoustics on the quality of care and physiological state of patients, Int J Cardiol, 98 (2)\ (2005), pp. 267-270 Feb 15
 S.T. Lawless, Crying wolf: false alarms in a pediatric intensive care unit, Crit Care Med, 22 (6) (1994), pp. 981-985 Jun
 C.L. Tsien, J.C. Fackler, Poor prognosis for existing monitors in the intensive care unit, Crit Care Med, 25 (4) (1997), pp. 614-619 Apr
 A. Mäkivirta, E. Koski, A. Kari, T. Sukuvaara, The median filter as a preprocessor for a patient monitor limit alarm system in intensive care, Comput Methods Programs Biomed, 34 (2-3) (1991), pp. 139-144 Feb-Mar
 Aboukhalil A, Nielsen L, Saeed M, Mark RG, Clifford GD. Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. J Biomed Inform 2008;41:442.
 Borowski M, Siebig S, Wrede C, Imhoff M. Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms. Comput Methods Programs Biomed 2011,
 Otero A, Felix P, Barro S, Palacios F. Addressing the flaws of current critical alarms: a fuzzy constraint satisfaction approach. Artif Intell Med 2009;47:219.
 Zong W, Moody GB, Mark RG. Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure. Med Biol Eng Comput \ 2004;42:698.
 Li Q, Mark RG, Clifford GD. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol Meas 2008;29:15.
 Li Q, Clifford GD. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals. Physiol Meas 2012;33:1491.
 Li Q, Mark RG, Clifford GD. Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator. Biomed Eng Online 2009;8:13
 J Behar, J Oster, Q Li, GD Clifford, ECG signal quality during arrhythmia and its application to false alarm reduction. IEEE Transactions on Biomedical Engineering 60 (6), 1660-1666, 2013.
 Q Li, C Rajagopalan, GD Clifford, Ventricular fibrillation and tachycardia classification using a machine learning approach, IEEE Transactions on Biomedical Engineering, 61 (6), 1607-1613, 2014.
 American National Standard (ANSI/AAMI EC13:2002) Cardiac monitors, heart rate meters, and alarms. Arlington, VA: Association for the Advancement of Medical Instrumentation; 2002.
Frequently asked questions about the Challenge
Can I get diagnostic output from my entry?
Diagnostic output is available if it was produced without access to test set data, and if the program does not use MATLAB. Output produced by MATLAB programs is not available (since MATLAB is not open source), and output produced from the test set is not available (since this would give away information about the hidden records.) If your entry does not use MATLAB, be sure to remove the line NEED_MATLAB=1 from setup.sh.
If your setup.sh fails (exits with non-zero status), anything it writes to the standard output is reported and the evaluation stops. Otherwise, no output from setup.sh is reported.
If your next.sh fails on a training set record, anything it writes to the standard output is reported and the evaluation stops (the remaining records are not tested). Otherwise, no output from next.sh is reported.
If your next.sh is unable to process a test set record within the time limit, it will receive poor partial scores for that record, but the evaluation will continue.
If your entry fails to process one or more test set records, or if it does not complete its analysis of one or more records within the per-record time limit, the numbers of failures and timeouts will be reported, along with the scores. No other diagnostic output relating to test set records will be reported.
May I submit a binary (executable) entry without a complete set of sources?
Entries that do not include a complete set of sources are ineligible for awards.
What version of MATLAB and what MATLAB toolboxes are available in the test environment?
The test environment has MATLAB 2014b and the WFDB Toolbox for MATLAB version 0.9.9. A list of installed MATLAB built-in toolboxes can be found here. Note that you must include the line NEED_MATLAB=1 in your setup.sh if you wish to use MATLAB in your entry.
If I use up all of my entries, can I register again using another email address and get more entries?
Please don't do this! The limit on the number of entries:
- is necessary in order to make it feasible to evaluate entries within a reasonable time as deadlines approach,
- reduces the opportunities for overfitting and consequent development of non-generalizable solutions, and
- is an important part of establishing a fair and level playing field for the Challenge.
Obviously we cannot prevent use of multiple email addresses to circumvent the limit on entries, but we consider such behavior abusive and disrespectful to other participants. If we discover that this has occurred, we will disqualify offending participants from this and future competitions.
Note, however, that participants are allowed (and encouraged) to collaborate in order to combine the best features of complementary approaches. If you wish to collaborate with another participant or team of participants, each of the team members may use the remaining entries of whichever collaborative team has the most remaining entries. Other than in exception circumstances, we expect that collaborators to be unknown to each other prior to the competition, and not from the same research group or department. Collaborators must be particularly careful to list all current team members in their AUTHORS files to avoid disqualification.
Is it worth analyzing the ECG?
Possibly. The monitors have excellent ECG analysis algorithms, but do not necessarily make use of all the information available. However, you may choose to use information in the ECG if you like. Be careful, though, as the signal may contain significant artifacts.
What are the alarm labels used for the 5 alarms? How are the alarms coded?
The alarms are coded using case sensitive strings in the following manner:
|Alarm Type||Coding String|
Listed below are the top-scoring programs submitted in the PhysioNet/Computing in Cardiology Challenge 2015. Please refer to the AUTHORS.txt and LICENSE.txt file included with each entry for information about attribution and licensing. For more information about the details of these algorithms, see the corresponding papers.
Event 1 (Real-Time)
Event 2 (Retrospective)
The papers below were presented at Computing in Cardiology 2015. 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.
The PhysioNet/Computing in Cardiology Challenge 2015: Reducing False Arrhythmia Alarms in the ICU
Gari Clifford, Ikaro Silva, Benjamin Moody, Qiao Li, Danesh Kella, Abdullah Shahin, Tristan Kooistra, Diane Perry, Roger Mark
The remaining papers were presented by participants in the Challenge, who describe their approaches to the challenge problem.
Identification of ECG Signal Pattern Changes to Reduce the Incidence of Ventricular Tachycardia False Alarms
Vytautas Abromavičius, Artūras Serackis, Andrius Gudiškis
Multi-modal Integrated Approach towards Reducing False Arrhythmia Alarms During Continuous Patient Monitoring: the PhysioNet Challenge 2015
Sardar Ansari, Ashwin Belle, Kayvan Najarian
Reduction of False Cardiac Arrhythmia Alarms Through the Use of Machine Learning Techniques
Miguel Caballero, Grace Mirsky
Suppression of False Arrhythmia Alarms Using ECG and Pulsatile Waveforms
Paula Couto, Ruben Ramalho, Rui Rodrigues
Heart Beat Fusion Algorithm to Reduce False Alarms for Arrhythmias
Chathuri Daluwatte, Lars Johannesen, Jose Vicente, Christopher G. Scully, Loriano Galeotti, David G. Strauss
Decreasing the False Alarm Rate of Arrhythmias in Intensive Care Using a Machine Learning Approach
Linda M. Eerikäinen, Joaquin Vanschoren, Michael J. Rooijakkers, Rik Vullings, Ronald M. Aarts
A Multimodal Approach to Reduce False Arrhythmia Alarms in the Intensive Care Unit
Sibylle Fallet, Sasan Yazdani, Jean-Marc Vesin
Algorithm for Life-Threatening Arrhythmias Detection with Reduced False Alarms Ratio
Iga Grzegorczyk, Kamil Ciuchciński, Jan Gierałtowski, Katarzyna Kośna, Piotr Podziemski, Mateusz Soliński
Reducing False Arrhythmia Alarms in the ICU Using Novel Signal Quality Indices Assessment Method
Runnan He, Henggui Zhang, Kuanquan Wang, Yongfeng Yuan, Qince Li, Jiabin Pan, Zhiqiang Sheng, Na Zhao
Reducing False Arrhythmia Alarms Using Robust Interval Estimation and Machine Learning
Christoph Hoog Antink, Steffen Leonhardt
Enhancing Accuracy of Arrhythmia Classification by Combining Logical and Machine Learning Techniques
Vignesh Kalidas, Lakshman Tamil
Validation of Arrhythmia Detection Library on Bedside Monitor Data for Triggering Alarms in Intensive Care
Vessela Krasteva, Irena Jekova, Remo Leber, Ramun Schmid, Roger Abaecherli
Reduction of False Alarms in Intensive Care Unit using Multi-feature Fusion Method
Chengyu Liu, Lina Zhao, Hong Tang
False Alarms in Intensive Care Unit Monitors: Detection of Life-threatening Arrhythmias Using Elementary Algebra, Descriptive Statistics and Fuzzy Logic
Filip Plesinger, Petr Klimes, Josef Halamek, Pavel Jurak
Reducing False Arrhythmia Alarms in the ICU by Hilbert QRS Detection
Nadi Sadr, Jacqueline Huvanandana, Doan Trang Nguyen, Chandan Kalra, Alistair McEwan, Philip de Chazal
Reducing False Arrhythmia Alarms in the ICU
Soo-Kng Teo, Jian Cheng Wong, Bo Yang, Feng Yang, Ling Feng, Toon Wei Lim, Yi Su
Reliability of Clinical Alarm Detection in Intensive Care Units
Charalampos Tsimenidis, Alan Murray
Multimodal Data Classification Using Signal Quality Indices and Empirical Similarity-Based Reasoning
Man Xu, Jiang Shen, Haiyan Yu
Anyone can access the files, as long as they conform to the terms of the specified license.
License (for files):
Open Data Commons Attribution License v1.0
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