Deep Learning Monitor

The role of automatic electrocardiogram (ECG) analysis in clinical practiceis limited by the accuracy of existing models. Deep Neural Networks (DNNs) aremodels composed of stacked transformations that learn tasks by examples. Thistechnology has recently achieved striking success in a variety of task andthere are great expectations on how it might improve clinical practice. Here wepresent a DNN model trained in a dataset with more than 2 million labeled examsanalyzed by the Telehealth Network of Minas Gerais and collected under thescope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. TheDNN outperform cardiology resident medical doctors in recognizing 6 types ofabnormalities in 12-lead ECG recordings, with F1 scores above 80% andspecificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.