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Automated Detection of Hypertension Using Physiological Signals: A Review

Author

Listed:
  • Manish Sharma

    (Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India)

  • Jaypal Singh Rajput

    (Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India)

  • Ru San Tan

    (National Heart Centre, Singapore 639798, Singapore)

  • U. Rajendra Acharya

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
    Department of Biomedical Engineering, School of Science and Technology, SUSS, Singapore 599494, Singapore)

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.

Suggested Citation

  • Manish Sharma & Jaypal Singh Rajput & Ru San Tan & U. Rajendra Acharya, 2021. "Automated Detection of Hypertension Using Physiological Signals: A Review," IJERPH, MDPI, vol. 18(11), pages 1-26, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5838-:d:564919
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    References listed on IDEAS

    as
    1. Manish Sharma & Jainendra Tiwari & U. Rajendra Acharya, 2021. "Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals," IJERPH, MDPI, vol. 18(6), pages 1-29, March.
    2. Jaypal Singh Rajput & Manish Sharma & U. Rajendra Acharya, 2019. "Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank," IJERPH, MDPI, vol. 16(21), pages 1-17, October.
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    Cited by:

    1. Jaypal Singh Rajput & Manish Sharma & T. Sudheer Kumar & U. Rajendra Acharya, 2022. "Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals," IJERPH, MDPI, vol. 19(7), pages 1-16, March.

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    1. Jaypal Singh Rajput & Manish Sharma & T. Sudheer Kumar & U. Rajendra Acharya, 2022. "Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals," IJERPH, MDPI, vol. 19(7), pages 1-16, March.
    2. Manish Sharma & Jainendra Tiwari & U. Rajendra Acharya, 2021. "Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals," IJERPH, MDPI, vol. 18(6), pages 1-29, March.

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