Abstract
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity 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.
Highlights
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models
The results indicate no statistically significant difference between the original Deep Neural Networks (DNNs) used in our analysis and the alternative models developed in the 90%-5%-5% splits
End-to-end learning presents an alternative to these two-step approaches, where the raw signal itself is used as an input to the classifier which learns, by itself, to extract the features
Summary
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12lead ECG recordings, with F1 scores above 80% and specificity 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. Deep neural networks (DNNs) have recently achieved striking success in tasks such as image classification[7] and speech recognition[8], and there are great expectations when it comes to how this technology may improve health care and clinical practice[9,10,11]. A convincing preliminary study of the use of DNNs in ECG analysis was recently presented in ref
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