Background Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. MethodsWe used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model.

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direct and indirect costs of patients with AF-related stroke in China, producing an average cost per patient per year and the economic burden of the whole AFrelated stroke Chinese population. METHODS: A cost-of-illness analyses was performed. Prevalence data on AF-related stroke for the Chinese population was collected from literatures. An observational retrospective study was conducted to collect the economic data. We recruited 156 patients diagnosed with AF and stroke in Beijing, Shanghai and Guangzhou between October 2012 and December 2012. Patients or their carers were interviewed about resource utilization and absenteeism from work in the past year. Direct medical costs included outpatient visit, hospitalization, ambulatory, drug, diagnostic tests, and physiotherapy costs. Indirect costs were estimated using a human capital approach. All costs referred to 2011. RESULTS: Among 156 patients with AFrelated stroke, 59.35% were male and the mean age was 67.9±30.2 years. 98.0% patients have at least one kind of health insurance. From the societal perspective, total costs per patient over 1 year amounted to Chinese Yuan (CNY) 25538 (median: CNY13342, IQR: CNY7662-CNY 38714), with direct costs accounting for 94.2% and indirect costs for 5.8% of the total. And for the direct costs, the informal care costs were CNY9162. The drug costs were CNY6293. Based on the prevalence of AF and AF-related stroke in China from literatures, there was about 0.968 million patients of AF-related stroke. Costs for the nation are estimated at CNY24.7 billion per year. CONCLUSIONS: The economic burden of AF-related stroke in China is considerable. The primary burden on patients was due to informal care and drugs.