Assessing computerized eye tracking technology for gaining insight into expert interpretation of the 12-lead electrocardiogram: an objective quantitative approach.

INTRODUCTION: It is well known that accurate interpretation of the 12-lead electrocardiogram (ECG) requires a high degree of skill. There is also a moderate degree of variability among those who interpret the ECG. While this is the case, there are no best practice guidelines for the actual ECG interpretation process. Hence, this study adopts computerized eye tracking technology to investigate whether eye-gaze can be used to gain a deeper insight into how expert annotators interpret the ECG. Annotators were recruited in San Jose, California at the 2013 International Society of Computerised Electrocardiology (ISCE).

METHODS: Each annotator was recruited to interpret a number of 12-lead ECGs (N=12) while their eye gaze was recorded using a Tobii X60 eye tracker. The device is based on corneal reflection and is non-intrusive. With a sampling rate of 60Hz, eye gaze coordinates were acquired every 16.7ms. Fixations were determined using a predefined computerized classification algorithm, which was then used to generate heat maps of where the annotators looked. The ECGs used in this study form four groups (3=ST elevation myocardial infarction [STEMI], 3=hypertrophy, 3=arrhythmias and 3=exhibiting unique artefacts). There was also an equal distribution of difficulty levels (3=easy to interpret, 3=average and 3=difficult). ECGs were displayed using the 4x3+1 display format and computerized annotations were concealed.

RESULTS: Precisely 252 expert ECG interpretations (21 annotators×12 ECGs) were recorded. Average duration for ECG interpretation was 58s (SD=23). Fleiss' generalized kappa coefficient (Pa=0.56) indicated a moderate inter-rater reliability among the annotators. There was a 79% inter-rater agreement for STEMI cases, 71% agreement for arrhythmia cases, 65% for the lead misplacement and dextrocardia cases and only 37% agreement for the hypertrophy cases. In analyzing the total fixation duration, it was found that on average annotators study lead V1 the most (4.29s), followed by leads V2 (3.83s), the rhythm strip (3.47s), II (2.74s), V3 (2.63s), I (2.53s), aVL (2.45s), V5 (2.27s), aVF (1.74s), aVR (1.63s), V6 (1.39s), III (1.32s) and V4 (1.19s). It was also found that on average the annotator spends an equal amount of time studying leads in the frontal plane (15.89s) when compared to leads in the transverse plane (15.70s). It was found that on average the annotators fixated on lead I first followed by leads V2, aVL, V1, II, aVR, V3, rhythm strip, III, aVF, V5, V4 and V6. We found a strong correlation (r=0.67) between time to first fixation on a lead and the total fixation duration on each lead. This indicates that leads studied first are studied the longest. There was a weak negative correlation between duration and accuracy (r=-0.2) and a strong correlation between age and accuracy (r=0.67).

CONCLUSIONS: Eye tracking facilitated a deeper insight into how expert annotators interpret the 12-lead ECG. As a result, the authors recommend ECG annotators to adopt an initial first impression/pattern recognition approach followed by a conventional systematic protocol to ECG interpretation. This recommendation is based on observing misdiagnoses given due to first impression only. In summary, this research presents eye gaze results from expert ECG annotators and provides scope for future work that involves exploiting computerized eye tracking technology to further the science of ECG interpretation.