Cardiff Metropolitan University
Browse

File(s) under embargo

Reason: 24 month embargo requested by publisher

10

month(s)

7

day(s)

until file(s) become available

Autoencoder and Machine Learning Method for Myocardial Infarction (MI) Detection Application

conference contribution
posted on 2023-08-08, 14:34 authored by H. Altorabi, Liqaa NawafLiqaa Nawaf

Our study presents a novel approach for myocardial infarction (MI) detection using an autoencoder and machine learning method. We propose a deep learning model that can automatically extract relevant features from electrocardiogram (ECG) signals to accurately diagnose MI. The proposed approach achieves state-of-the-art performance with an accuracy of 99.6% on a large-scale ECG dataset. Furthermore, we conducted extensive experiments to evaluate the robustness and generalizability of our model, which shows promising results. Our work has the potential to improve the diagnosis of MI and reduce medical errors, leading to better patient outcomes.

History

Presented at

6th Smart Cities Symposium (SCS 2022) 6-8 Dec. 2022 Hybrid Conference, Bahrain

Published in

6th Smart Cities Symposium (SCS 2022)

Publisher

IET

Version

  • AM (Accepted Manuscript)

Citation

Altorabi, H., & Nawaf, L. (2022, December). Autoencoder and machine learning method for myocardial infarction (MI) detection application. In 6th Smart Cities Symposium (SCS 2022) (Vol. 2022, pp. 136-140). IET.

ISBN

978-1-83953-854-4

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Liqaa Nawaf

Copyright Holder

  • © The Publisher

Publisher Rights Statement

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Language

  • en

Usage metrics

    School of Technologies Research - Conference Papers

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC