Daniel Guldenring

TL;DR: Two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn) are investigated for improved automated detection of AF from the ECG, and a new database from ECG data taken from existing sources is compiled.

TL;DR: Eye tracking facilitated a deeper insight into how expert annotators interpret the 12-lead ECG, and the authors recommend ECG annotators to adopt an initial first impression/pattern recognition approach followed by a conventional systematic protocol to ECG interpretation.

TL;DR: It is found that an incorrect AD disturbs the reliability of interpreter confidence in predicting accuracy and reduces the predictive power of confidence for predicting accuracy (even more so for non-CFs).

TL;DR: There is a reasonable chance this particular incorrect electrode configuration will change the diagnosis of the 12-lead ECG, which highlights the importance of developing algorithms to detect electrode misplacement along with better education regarding ECG acquisition.

TL;DR: This research develops a transformation (ML2VCG) by means of multivariate linear regression on a training data set of 545 ML 12-lead ECGs and corresponding Frank VCGs that were both extracted surface potential maps (BSPMs).