health life sciences medicine
This dataset contains collected eye tracking data from medical practitioners and students with different expertise levels to understand their electrocardiogram interpretation behavior. These medical practitioners encounter electrocardiograms during their daily medical practice. The end goal of the collection of this dataset was to use it for analysis to uncover insights about key best practices as well as pitfall in the ECG interpretation process. The data consists of quantitative eye tracking measurements for different eye gaze features for 63 participants. These participants belong to six expertise categories, namely: medical students, nurses, technicians, residents, fellows and finally consultants. Each one of these participants contributed to the data collection by interpreting ten different electrocardiograms. The eye tracking data includes the eye fixations count and duration, the eye gaze duration, and finally the fixations revisitations. It was collected at 60 frames per second using a Tobii Pro X2-60. Each participant was allowed a timeframe of 30 seconds to interpret each one of the ten ECGs. The collected data was processed taking into consideration two defined area of interest distributions. This enabled the extraction of metrics specifically tailored for the 12-lead electrocardiogram.
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Open Data Commons Open Database License v1.0
https://doi.org/10.13026/gsr5-8b11
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Eye Tracking Dataset for the 12-Lead Electrocardiogram Interpretation of Medical Practitioners and Students was accessed on DATE from https://registry.opendata.aws/eye-tracking-ecg.
arn:aws:s3:::physionet-open/eye-tracking-ecg/us-east-1aws s3 ls --no-sign-request s3://physionet-open/eye-tracking-ecg/