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Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank

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  • Jaypal Singh Rajput

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

  • Manish Sharma

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

  • U. Rajendra Acharya

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
    Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
    International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan)

Abstract

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t -test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4068-:d:279324
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    References listed on IDEAS

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    1. Brown, T.M. & Cueto, M. & Fee, E., 2006. "The World Health Organization and the transition from international to global public health," American Journal of Public Health, American Public Health Association, vol. 96(1), pages 62-72.
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    Cited by:

    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 & 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.
    3. 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.

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