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AzTrials

Arrhythmias, CardiacAI-ECGNCT03662802

Development of a Novel Convolution Neural Network for Arrhythmia Classification

An observational study of Arrhythmias, Cardiac and Cardiac Arrest, run by Scripps Clinic at 1 site in United States.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record
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Trial summary

Identifying the correct arrhythmia at the time of a clinic event including cardiac arrest is of high priority to patients, healthcare organizations, and to public health. Recent developments in artificial intelligence and machine learning are providing new opportunities to rapidly and accurately diagnose cardiac… Read more about this study

Who is it for?
Any ageHealthy volunteers can take part
What is being tested?
Neural Network Classifier
How many people
25,458 participants
Where
1 siteUnited States
Study dates
1 Oct 2018to 1 Oct 2020
How long will I take part?
1 Year
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages Any age
  • All sexes
  • Healthy volunteers accepted
  • Child, Adult, Older adult

Study population: Individuals undergoing a 12-lead ECG or Holter/Event monitoring

Who may be able to join

  • All ECG data compiled from 12-lead ECG, single, and multiple lead databases

Who cannot join

  • None

Trial locations and contacts

1 site in 1 country.

United States1 site

  • Scripps Clinic

    San Diego, California, 92037

Other trials in United States

About this study

Official title: Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms

Identifying the correct arrhythmia at the time of a clinic event including cardiac arrest is of high priority to patients, healthcare organizations, and to public health. Recent developments in artificial intelligence and machine learning are providing new opportunities to rapidly and accurately diagnose cardiac arrhythmias and for how new mobile health and cardiac telemetry devices are used in patient care. The current investigation aims to validate a new artificial intelligence statistical approach called 'convolution neural network classifier' and its performance to different arrhythmias diagnosed on 12-lead ECGs and single-lead Holter/event monitoring. These arrhythmias include; atrial fibrillation, supraventricular tachycardia, AV-block, asystole, ventricular tachycardia and ventricular fibrillation, and will be benchmarked to the American Heart Association performance criteria (95% one-sided confidence interval of 67-92% based on arrhythmia type). In order to do so, the study approach is to create a large ECG database of de-identified raw ECG data, and to train the neural network on the ECG data in order to improve the diagnostic accuracy.

Being tested
Neural Network Classifier
Keywords
artificial intelligence, machine learning, neural network, cardiac arrhythmia, ECG, EKG
Medical subject headings
Arrhythmias, Cardiac, Heart Arrest

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Neural Network ClassifierOther

    The convolutional neural network is configured to receive an electrocardiogram segment as an input and to generate an output indicative of whether the received electrocardiogram segment represents a cardiac arrhythmia. No specific features of the electrocardiogram are identified to the convolutional neural network, and the received electrocardiogram segment is not filtered, transformed, or processed prior to reception by the algorithm. The algorithm is trained in a similar manner - the electrocardiogram segments are the sole input to the convolutional neural network.

Study groups in NCT03662802
Study groupRoleWhat they receive
ECG DataCoded data including; wavelengths, amplitude, intervals, timing, frequence
Receives: Other: Neural Network Classifier

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Diagnostic Accuracy

    American Heart Association ECG Performance Criteria

    Measured: 1 YEAR

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted7 Sep 2018
  2. Study start1 Oct 2018
  3. Primary completion1 Mar 2020
  4. Study completion1 Oct 2020

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Other
Enrolment
25,458 participants (actual)
Target duration
1 Year
IDs, dates and publications
ClinicalTrials.gov ID
NCT03662802
Sponsor's protocol ID
027527
Acronym
AI-ECG
Status last verified
Nov 2020
First submitted
5 Sep 2018
Last updated
6 Nov 2020
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. doi: 10.1016/j.jacc.2018.03.521.

    PubMed 29880128

  • Kiranyaz S, Ince T, Gabbouj M. Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Trans Biomed Eng. 2016 Mar;63(3):664-75. doi: 10.1109/TBME.2015.2468589. Epub 2015 Aug 14.

    PubMed 26285054

  • Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.

    PubMed 29169478

  • Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J. 2016 May 7;37(18):1428-38. doi: 10.1093/eurheartj/ehv770. Epub 2016 Feb 11.

    PubMed 26873093

  • Vandendriessche B, Abas M, Dick TE, Loparo KA, Jacono FJ. A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features. IEEE Trans Biomed Eng. 2017 Dec;64(12):2890-2900. doi: 10.1109/TBME.2017.2684244. Epub 2017 Mar 17.

    PubMed 28328498

  • Arvanaghi R, Daneshvar S, Seyedarabi H, Goshvarpour A. Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification. Comput Methods Programs Biomed. 2017 Nov;151:71-78. doi: 10.1016/j.cmpb.2017.08.013. Epub 2017 Aug 24.

    PubMed 28947007

  • Figuera C, Irusta U, Morgado E, Aramendi E, Ayala U, Wik L, Kramer-Johansen J, Eftestol T, Alonso-Atienza F. Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators. PLoS One. 2016 Jul 21;11(7):e0159654. doi: 10.1371/journal.pone.0159654. eCollection 2016.

    PubMed 27441719

  • Li Q, Rajagopalan C, Clifford GD. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng. 2014 Jun;61(6):1607-13. doi: 10.1109/TBME.2013.2275000. Epub 2013 Jul 26.

    PubMed 23899591

  • Lyon A, Minchole A, Martinez JP, Laguna P, Rodriguez B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J R Soc Interface. 2018 Jan;15(138):20170821. doi: 10.1098/rsif.2017.0821.

    PubMed 29321268

  • Mjahad A, Rosado-Munoz A, Bataller-Mompean M, Frances-Villora JV, Guerrero-Martinez JF. Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning. Comput Methods Programs Biomed. 2017 Apr;141:119-127. doi: 10.1016/j.cmpb.2017.02.010. Epub 2017 Feb 10.

    PubMed 28241963

  • Xiong Z, Nash MP, Cheng E, Fedorov VV, Stiles MK, Zhao J. ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol Meas. 2018 Sep 24;39(9):094006. doi: 10.1088/1361-6579/aad9ed.

    PubMed 30102248

  • Fan X, Yao Q, Cai Y, Miao F, Sun F, Li Y. Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings. IEEE J Biomed Health Inform. 2018 Nov;22(6):1744-1753. doi: 10.1109/JBHI.2018.2858789. Epub 2018 Aug 7.

    PubMed 30106699

  • Warrick PA, Nabhan Homsi M. Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection. Physiol Meas. 2018 Oct 30;39(11):114002. doi: 10.1088/1361-6579/aad386.

    PubMed 30010088

  • Shen CP, Freed BC, Walter DP, Perry JC, Barakat AF, Elashery ARA, Shah KS, Kutty S, McGillion M, Ng FS, Khedraki R, Nayak KR, Rogers JD, Bhavnani SP. Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator. J Am Heart Assoc. 2023 Apr 18;12(8):e026974. doi: 10.1161/JAHA.122.026974. Epub 2023 Mar 21.

    PubMed 36942628

Questions about this trial

What is this clinical trial studying?

An observational study of Arrhythmias, Cardiac and Cardiac Arrest, run by Scripps Clinic at 1 site in United States.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: finished, as of the sponsor's last update on 6 Nov 2020. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Diagnostic Accuracy (time frame: 1 YEAR). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT03662802, which also lists 14 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Nov 2020.

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