Clinical trial NCT03662802: Development of a Novel Convolution Neural Network for Arrhythmia Classification
Official title: Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Arrhythmias, Cardiac
Cardiac Arrest
Cardiac Arrythmias
Keywords: artificial intelligence
machine learning
neural network
cardiac arrhythmia
ECG
EKG
Lead sponsor: Scripps Health
Sponsor class: OTHER
First posted: 2018-09-07
Enrollment: 25458
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Individuals undergoing a 12-lead ECG or Holter/Event monitoring

Brief 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 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.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* None

Primary outcomes:
measure: Diagnostic Accuracy
timeFrame: 1 YEAR
Secondary outcomes:
Other outcomes:
Locations:
city: San Diego
country: United States
facility: Scripps Clinic
state: California
Study officials:
affiliation: Scripps Clinic Medical Group
name: Sanjeev Bhavnani, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.