Analysis of the Importance of Systolic Blood Pressure Versus Diastolic Blood Pressure in Diagnosing Hypertension: MRA Study.

Document Type

Conference Proceeding

Source of Publication

Proceedings - International Conference on Image Processing, ICIP

Publication Date

10-1-2020

Abstract

© 2020 IEEE. Hypertension is one of the severest and most common diseases nowadays. It is considered one of the leading contributors to death worldwide. Specialists tend to diagnose hypertension taking into consideration both systolic and diastolic blood pressure (BP) measurements. However, some clinical hypothesis states that under 50 years of age, diastolic may be slightly more predictive of adverse events, while above that age, systolic may be more predictive. The question is should we give more value to systolic BP or diastolic BP when diagnosing diseases such as hypertension? Three different experiments were conducted in this study using magnetic resonance angiography (MRA) data to investigate this question. In each of these experiments, the following methodology was followed: 1) preprocess MRA data to remove noise, bias, or inhomogeneities, 2) segment the cerebral vasculature for each subject using a CNN-based approach, 3) extract vascular features that represent cerebral alterations that precede and accompany the development of hypertension, and 4) finally build feature vectors and classify data into either normotensives or hypertensives based on the cerebral alterations and the blood pressure measurements. The first experiment was conducted on original data set of 342 subjects. While the second and third experiments enlarged the original data set by generating more synthetic samples to make original data set large enough and balanced. Experimental results showed that systolic blood pressure might be more predictive than diastolic blood pressure in diagnosing hypertension with a classification accuracy of 89.3%.

ISBN

9781728163956

ISSN

1522-4880

Publisher

IEEE

Volume

2020-October

First Page

443

Last Page

447

Disciplines

Computer Sciences

Keywords

Blood Vessels, Cerebral, CNN, Hypertension, Logistic Regression., SVM, Tortuosity

Scopus ID

85098655811

Indexed in Scopus

yes

Open Access

no

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