Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient’s cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

死于 COVID-19 的患者通常患有心血管疾病。基于可穿戴医疗设备的实时心血管疾病监测可以有效降低COVID-19死亡率。然而，由于技术限制，存在三个主要问题。首先，传统的可穿戴医疗设备无线通信技术难以完全满足实时性要求。其次，目前的监测平台缺乏高效的流式数据处理机制来应对实时产生的大量心血管数据。第三，监测平台的诊断通常是人工进行的，很难保证足够多的在线医生提供及时、高效、准确的诊断。为了解决这些问题，本文提出了一种使用深度学习的 5G 实时心血管监测系统，用于 COVID-19 患者。首先，我们利用 5G 来发送和接收可穿戴医疗设备的数据。其次，应用Flink流数据处理框架来访问心电图数据。最后，我们使用卷积神经网络和长短期记忆网络模型来自动预测COVID-19患者的心血管健康状况。理论分析和实验结果表明，我们的建议可以很好地解决上述问题，将心血管疾病的预测准确率提高到99.29%。