Abstract
In recent years, cardiovascular-related diseases have become the "number one killer" threatening human life and health and have received much attention. The timely and accurate detection and diagnosis of arrhythmias and heart failure are relatively common heart diseases, which are of great social value and research significance in improving people's quality of life by providing early treatment or intervention for those who are at risk. Based on this, this paper proposes a deep learning network architecture based on the combination of long- and short-term memory networks and deep residual neural networks for the automatic detection of heart failure. A total of 60 elderly patients with severe heart failure treated in the emergency department of our hospital from August 2019 to August 2021 were selected as the sample subjects of this study. The treatment outcomes and prognostic quality of life of the two groups of patients were compared and analyzed. Based on the unbiased test method, the accuracy of the proposed method on the authoritative open continuous heart rate database PhysioNet was 99.67% (data length 500), 98.84% (data length 1000), and 96.63% (data length 2000). This indicates that the network model can well extract the high-dimensional features of continuous heart rate and improve the accuracy of the classification model. The LSTM neural model proposed in this paper may be able to provide richer information on heart health status for portable ECG detection systems, which have very important clinical value and social significance.