Sharma Manish, Rajput Jaypal Singh, Tan Ru San, Acharya U Rajendra
Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India.
National Heart Centre, Singapore 639798, Singapore.
Int J Environ Res Public Health. 2021 May 29;18(11):5838. doi: 10.3390/ijerph18115838.
Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.
动脉高血压（HT）是一种血压（BP）升高的慢性疾病，可能导致心血管疾病、中风、肾衰竭的发病率增加以及死亡率上升。如果早期诊断出HT，有效的治疗可以控制血压并避免不良后果。诸如心电图（ECG）、光电容积脉搏波描记法（PPG）、心率变异性（HRV）和心冲击图（BCG）等生理信号可用于监测健康状况，但与血压测量并无直接关联。使用这些生理信号手动检测HT既耗时又容易出现人为误差。因此，已经开发了许多计算机辅助诊断系统。本文是对使用ECG、HRV、PPG和BCG信号自动检测HT的研究进行的系统综述。在本综述中，我们从250篇筛选论文中确定了23项符合我们纳入标准的研究。讨论了研究方法、所研究的生理信号、使用的数据库、采用的各种非线性技术、特征提取以及诊断性能参数的详细信息。基于ECG和HRV信号的机器学习和深度学习方法取得了最佳性能，可用于开发HT的计算机辅助诊断。这项工作提供了一些见解，可能有助于开发基于ECG和HRV信号的用于连续无袖带远程血压监测的可穿戴设备。
