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Diagnostics
Special Issues
Applications of Artificial Intelligence in ECG
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Applications of Artificial Intelligence in ECG

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (10 October 2022) | Viewed by 4060

Special Issue Editor

Dr. Joon-myoung Kwon

E-Mail Website
Guest Editor
1. Department of Emergency Medicine, Incheon Sejong Hospital, Incheon, Korea
2. Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Korea
3. Medical AI, Inc., San Francisco, CA, USA
Interests: deep learning; electrocardiography; biosignal; critical care medicine; cardiology; emergency medicine

Special Issue Information

Dear Colleagues,

Since 2019, electrocardiogram (ECG) analysis based on deep learning has been investigated to enable the diagnosis of diseases that cannot be diagnosed using conventional ECG. Recent studies have shown that deep learning-enabled ECG can be employed to detect heart failure, pulmonary hypertension, hyperkalemia, and many other diseases. Various technologies based on deep learning have been discovered, such as the generation of precordial six-lead ECGs from limb six-lead ECGs. The use of deep learning for analyzing ECG needs to be validated more precisely, to enable it to be used in the real clinical environment and to provide solid insights to discover novel medical knowledge.

Dr. Joon-myoung Kwon
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • electrocardiography
  • machine learning

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Published Papers (1 paper)

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Research

19 pages, 1915 KiB  
Article
Electrocardiogram (ECG)-Based User Authentication Using Deep Learning Algorithms
by Vibhav Agrawal, Mehdi Hazratifard, Haytham Elmiligi and Fayez Gebali
Diagnostics 2023, 13(3), 439; https://doi.org/10.3390/diagnostics13030439 - 25 Jan 2023
Cited by 10 | Viewed by 3647
Abstract
Personal authentication security is an essential area of research in privacy and cybersecurity. For individual verification, fingerprint and facial recognition have proved particularly useful. However, such technologies have flaws such as fingerprint fabrication and external impediments. Different AI-based technologies have been proposed to [...] Read more.
Personal authentication security is an essential area of research in privacy and cybersecurity. For individual verification, fingerprint and facial recognition have proved particularly useful. However, such technologies have flaws such as fingerprint fabrication and external impediments. Different AI-based technologies have been proposed to overcome forging or impersonating authentication concerns. Electrocardiogram (ECG)-based user authentication has recently attracted considerable curiosity from researchers. The Electrocardiogram is among the most reliable advanced techniques for authentication since, unlike other biometrics, it confirms that the individual is real and alive. This study utilizes a user authentication system based on electrocardiography (ECG) signals using deep learning algorithms. The ECG data are collected from users to create a unique biometric profile for each individual. The proposed methodology utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to analyze the ECG data. The CNNs are trained to extract features from the ECG data, while the LSTM networks are used to model the temporal dependencies in the data. The evaluation of the performance of the proposed system is conducted through experiments. It demonstrates that it effectively identifies users based on their ECG data, achieving high accuracy rates. The suggested techniques obtained an overall accuracy of 98.34% for CNN and 99.69% for LSTM using the Physikalisch–Technische Bundesanstalt (PTB) database. Overall, the proposed system offers a secure and convenient method for user authentication using ECG data and deep learning algorithms. The approach has the potential to provide a secure and convenient method for user authentication in various applications. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in ECG)
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