Development of a Novel Convolution Neural Network for Arrhythmia Classification

Cardiac Arrest Clinical Trial

— AI-ECG  

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03662802
• Other study ID #: 027527
• Status: Completed
• Start date: October 1, 2018
• Est. completion date: October 1, 2020

Study information

• Verified date: November 2020
• Source: Scripps Health
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical 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 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 25458
• Est. completion date: October 1, 2020
• Est. primary completion date: March 1, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

• None

Related Conditions & MeSH terms

• Arrhythmias, Cardiac
• Cardiac Arrest
• Cardiac Arrythmias

Intervention

Other:
• Neural Network Classifier
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.

Locations

Country	Name	City	State
United States	Scripps Clinic	San Diego	California

Sponsors (1)

Lead Sponsor	Collaborator
Scripps Clinic

Country where clinical trial is conducted

United States, 

References & Publications (13)

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. — View 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. Review. — View 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 Preci — View 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.2018.28587 — View Citation

Figuera C, Irusta U, Morgado E, Aramendi E, Ayala U, Wik L, Kramer-Johansen J, Eftestøl 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 — View 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. Review. — View 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. — View 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. — View Citation

Lyon A, Mincholé A, Martínez 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). pii: 20170821. doi: 10.1098/rsif.2017.0821. Revie — View Citation

Mjahad A, Rosado-Muñoz A, Bataller-Mompeán M, Francés-Víllora JV, Guerrero-Martínez JF. Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning. Comput Methods Pr — View 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. — View 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. — View 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. — View Citation

* Note: There are 13 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic Accuracy	American Heart Association ECG Performance Criteria	1 YEAR