In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.
Objective To summarize and analyze the characteristics, advantages and disadvantages of the current governance framework for public health emergencies in China. Methods The CNKI, VIP, WanFang Data, CBM and PubMed databases were electronically searched to collect studies on the management of major infectious disease outbreaks in China from inception to April 15, 2023. The basic information and governance elements included in the study were extracted and analyzed. Results A total of 30 studies were included, and the time of issuance was from 2020 to 2022. Most of the studies were on COVID-19, focusing on the governance framework of big data governance, holistic governance, and multi-agent collaborative governance. The governance elements were mainly concentrated in three aspects: governance subject, governance cycle and institutional guarantee. The governance entities were concentrated on multi-agent collaborative governance, with the governance cycle mainly focused on in process governance, and the basic guarantee is a multiple guarantee with information technology big data as the main body. Conclusion The governance body of China's major infectious disease epidemic management framework has transitioned from a single entity to a multi entity collaborative governance. While increasing prewarning governance, attention should also be paid to governance during the post recovery period. In terms of system, comprehensive guarantees such as epidemic public opinion control system guarantees, privacy security guarantees, and psychological counseling guarantees should be added.