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Open AccessArticle

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

1
Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India
2
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3
Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
4
International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(21), 4068; https://doi.org/10.3390/ijerph16214068
Received: 31 August 2019 / Revised: 13 October 2019 / Accepted: 14 October 2019 / Published: 23 October 2019
(This article belongs to the Section Digital Health)
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. View Full-Text
Keywords: hypertension; ECG; wavelets; optimization; semidefinite program; filter design hypertension; ECG; wavelets; optimization; semidefinite program; filter design
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MDPI and ACS Style

Rajput, J.S.; Sharma, M.; Acharya, U.R. Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank. Int. J. Environ. Res. Public Health 2019, 16, 4068. https://doi.org/10.3390/ijerph16214068

AMA Style

Rajput JS, Sharma M, Acharya UR. Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank. International Journal of Environmental Research and Public Health. 2019; 16(21):4068. https://doi.org/10.3390/ijerph16214068

Chicago/Turabian Style

Rajput, Jaypal S.; Sharma, Manish; Acharya, U. R. 2019. "Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank" Int. J. Environ. Res. Public Health 16, no. 21: 4068. https://doi.org/10.3390/ijerph16214068

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