Key result
A novel hypertension diagnosis index using ECG signal fractional dimension and log-energy features was developed to discriminate low-risk and high-risk hypertension.
Why the study?
Hypertension is asymptomatic early on and visual ECG analysis is tedious and prone to human error, necessitating an automated ECG-based diagnostic system.
Can an automated ECG-based system using a novel hypertension diagnosis index discriminate between low-risk and high-risk hypertension?
Can an automated ECG-based system using a novel hypertension diagnosis index discriminate between low-risk and high-risk hypertension?
A novel ECG-based hypertension diagnosis index using wavelet filter bank features may help automate the identification of high-risk hypertension patients.
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.
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Rajput et al. (2019) studied Hypertension (n=139). Hypertension diagnosis index (HDI) using optimal orthogonal wavelet filter bank (OWFB) was evaluated on Discrimination of low-risk hypertension (LRHT) and high-risk hypertension (HRHT). A novel hypertension diagnosis index using ECG signal fractional dimension and log-energy features was developed to discriminate low-risk and high-risk hypertension.
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