Interpretation of a 12-Lead Electrocardiogram by Medical Students: Quantitative Eye-Tracking Approach

Mohammed Tahri Sqalli; Dena Al-Thani; Mohamed B Elshazly; Mohammed Al-Hijji

doi:10.2196/26675

Interpretation of a 12-Lead Electrocardiogram by Medical Students: Quantitative Eye-Tracking Approach

JMIR Med Educ. 2021 Oct 14;7(4):e26675. doi: 10.2196/26675.

Authors

Mohammed Tahri Sqalli¹, Dena Al-Thani¹, Mohamed B Elshazly^{2

3}, Mohammed Al-Hijji^{2

4}

Affiliations

¹ Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
² Weill Cornell Medical College, Cornell University, Doha, Qatar.
³ Department of Cardiac Electrophysiology, Cleveland Clinic, Cleveland, OH, United States.
⁴ Interventional & Structural Cardiology Division, Hamad Medical Corporation, Doha, Qatar.

PMID: 34647899
PMCID: PMC8554676
DOI: 10.2196/26675

Abstract

Background: Accurate interpretation of a 12-lead electrocardiogram (ECG) demands high levels of skill and expertise. Early training in medical school plays an important role in building the ECG interpretation skill. Thus, understanding how medical students perform the task of interpretation is important for improving this skill.

Objective: We aimed to use eye tracking as a tool to research how eye fixation can be used to gain a deeper understanding of how medical students interpret ECGs.

Methods: In total, 16 medical students were recruited to interpret 10 different ECGs each. Their eye movements were recorded using an eye tracker. Fixation heatmaps of where the students looked were generated from the collected data set. Statistical analysis was conducted on the fixation count and duration using the Mann-Whitney U test and the Kruskal-Wallis test.

Results: The average percentage of correct interpretations was 55.63%, with an SD of 4.63%. After analyzing the average fixation duration, we found that medical students study the three lower leads (rhythm strips) the most using a top-down approach: lead II (mean=2727 ms, SD=456), followed by leads V1 (mean=1476 ms, SD=320) and V5 (mean=1301 ms, SD=236). We also found that medical students develop a personal system of interpretation that adapts to the nature and complexity of the diagnosis. In addition, we found that medical students consider some leads as their guiding point toward finding a hint leading to the correct interpretation.

Conclusions: The use of eye tracking successfully provides a quantitative explanation of how medical students learn to interpret a 12-lead ECG.

Keywords: ECG; ECG interpretation; electrocardiogram; eye; eye tracking; human-computer interaction; interpretation; medical education; medical student; tracking.

©Mohammed Tahri Sqalli, Dena Al-Thani, Mohamed B Elshazly, ‪Mohammed Al-Hijji. Originally published in JMIR Medical Education (https://mededu.jmir.org), 14.10.2021.