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Examining Human ECG Interpretability Through Eye Movement to Improve Automated Outcomes

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submitted on 2024-10-27, 10:42 and posted on 2024-11-03, 09:06 authored by Mohammed Tahri Sqalli
The electrocardiogram (ECG) has been the de-facto tool for graphically representing the electrical activity of the human heart. The 12-lead ECG displays this activity from 12 ”perspectives” called ”leads”, in relation to the position of the 12 sensors attached to the body. The ECG is a practical and affordable diagnostic medical test, used in medical settings around the world. Despite the fact that more than three hundred million electrocardiograms are performed each year, the task of correctly interpreting them is considered a tedious and complex one. Failing to correctly interpret an ECG may result in injury or death. Therefore, several attempts at automating the interpretation of ECGs were made and are being developed with the advent of machine learning. Yet, these automation efforts are still aspiring to reach a sufficiently reliable level compared to human interpretation. Since the task requires both cognitive and visual efforts, eye tracking is a suitable methodology to reveal the dynamics of the interpretation procedure. This thesis aims at revealing indicators in the visual behavior of the interpreter that specify nuances useful in forthcoming ECG interpretation accuracy. This work uses eye-tracking as an enabling methodology to analyze how different health practitioners proceed into interpreting an ECG with relation to the accuracy of interpretation. Results of this work included quantifying the eye movement behavior of medical practitioners using eye tracking data, mainly fixations and gaze data, and correlating it with the roles of medical practitioners reflected through their accuracy diagnosing different heart arrhythmias.

History

Language

  • English

Publication Year

  • 2022

License statement

© The author. The author has granted HBKU and Qatar Foundation a non-exclusive, worldwide, perpetual, irrevocable, royalty-free license to reproduce, display and distribute the manuscript in whole or in part in any form to be posted in digital or print format and made available to the public at no charge. Unless otherwise specified in the copyright statement or the metadata, all rights are reserved by the copyright holder. For permission to reuse content, please contact the author.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

Degree Date

  • 2022

Degree Type

  • Doctorate

Advisors

Dena Al-Thani ; B. Elshazly Mohamed

Committee Members

Caroline Jay ; Borbala Mifsud ; Marwa Qaraqe ; Jens Schneider ; Castrillon Menezes Brenno

Department/Program

College of Science and Engineering

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