Automated detection of hypertension using physiological signals: A review
Article
Sharma, Manish, Rajput, J.S., Tan, Ru San and Acharya, U. Rajendra. 2021. "Automated detection of hypertension using physiological signals: A review." International Journal of Environmental Research and Public Health. 18 (11). https://doi.org/10.3390/ijerph18115838
Article Title | Automated detection of hypertension using physiological signals: A review |
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ERA Journal ID | 44293 |
Article Category | Article |
Authors | Sharma, Manish, Rajput, J.S., Tan, Ru San and Acharya, U. Rajendra |
Journal Title | International Journal of Environmental Research and Public Health |
Journal Citation | 18 (11) |
Article Number | 5838 |
Number of Pages | 26 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1660-4601 |
1661-7827 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph18115838 |
Web Address (URL) | https://www.mdpi.com/1660-4601/18/11/5838 |
Abstract | 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. |
Keywords | ANN; hypertension; ECG signal; HRV signal; BCG signal; PPG signal; deep learning; CNN; ANN; RNN; supervised machine learning; HT ECG signal classification |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Institute of Infrastructure, Technology, Research and Management (IITRAM), India |
National Heart Centre, Singapore | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1w1q/automated-detection-of-hypertension-using-physiological-signals-a-review
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