Usefulness of Machine-Learning-Based Detection and Classification of Cardiac Arrhythmias with 12-Lead Electrocardiograms

Abstract Background Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias by using a single-lead ECG input data set have been developed. It remains to be determined whether these algorithms can be generalized to 12-lead, ECG-based rhythm classification. Methods We employed a long short-term memory (LSTM) model to detect 12 heart rhythm classes by using 65,932 digital 12-lead ECG signals from 38,899 patients, using annotations obtained by consensus of three board-certified electrophysiologists as the gold standard. Results The accuracy of the LSTM model for the classification of each of the 12 heart rhythms was ≥0.982 (range: 0.982–1.0), with an area under the curve of ≥0.987 (range: 0.987–1.0). The precision and recall ranged from 0.692 to 1 and from 0.625 to 1, respectively, with an F1 score of ≥0.777 (range: 0.777–1.0). The accuracy of the model (0.90) was superior to the mean accuracies of internal-medicine specialists (0.55), emergency physicians (0.73), and cardiologists (0.83). Conclusions We demonstrated the feasibility and effectiveness of the deep-learning LSTM model for interpreting 12 common heart rhythms according to 12-lead ECG signals. The findings may have clinical relevance for the early diagnosis of cardiac-rhythm disorders.