摘要：SummaryElectrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model, which was ranked first in the challenge competition, achieved a median overall F1-score of 0.84 for the nine-type CA classification of CPSC2018's hidden test set of 2,954 ECG recordings. Further analysis showed that concurrent CAs were adequately predictive for 476 patients with multiple types of CA diagnoses in the dataset. Using only single-lead data yielded a performance that was only slightly worse than using the full 12-lead data, with leads aVR and V1 being the most prominent. We extensively consider these results in the context of their agreement with and relevance to clinical observations.Graphical AbstractDisplay OmittedHighlights•Accurate AI diagnosis of cardiac arrhythmia on ECG data from 11 hospitals•Capable of diagnosing concurrent cardiac arrhythmias•An ensemble model combining 12- and 1-lead models ranked first in CPSC2018•aVR and V1 found to be the best-performing single leadsDiagnostics; Arrhythmology; Artificial Intelligence
