Decoding 2.3 Million ECGs: Interpretable Deep Learning for Advancing Cardiovascular Diagnosis and Mortality Risk Stratification

Authors:
raw_author_name: Lei Lu
raw_affiliation_string: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford , Oxford , UK; School of Life Course & Population Sciences, King’s College London , London , UK
raw_author_name: Tingting Zhu
raw_affiliation_string: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford , Oxford , UK
raw_author_name: Antonio H Ribeiro
raw_affiliation_string: Department of Information Technology, Uppsala University , Uppsala , Sweden
raw_author_name: Jiandong Zhou
raw_affiliation_string: Division of Health Science, Warwick Medical School, University of Warwick , Coventry , UK
raw_author_name: Antonio Luiz P Ribeiro
raw_affiliation_string: Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Cl´ınicas, Universidade Federal de Minas Gerais , Belo Horizonte , Brazil
raw_author_name: Yuan-Ting Zhang
raw_affiliation_string: Department of Biomedical Engineering, City University of Hong Kong , Hong Kong SAR , China; Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park , Hong Kong SAR , China
raw_author_name: David A Clifton
raw_affiliation_string: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford , Oxford , UK; Oxford Suzhou Centre for Advanced Research , Suzhou , China

Abstract: Abstract Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.