Background

Hypertension (HPT) occurs when there is increase in blood pressure (BP) within the arteries, causing the heart to pump harder against a higher afterload to deliver oxygenated blood to other parts of the body.

Purpose

Due to fluctuation in BP, 24-h ambulatory blood pressure monitoring has emerged as a useful tool for diagnosing HPT but is limited by its inconvenience. So, an automatic diagnostic tool using electrocardiogram (ECG) signals is used in this study to detect HPT automatically.

Method

The pre-processed signals are fed to a convolutional neural network model. The model learns and identifies unique ECG signatures for classification of normal and hypertension ECG signals. The proposed model is evaluated by the 10-fold and leave one out patient based validation techniques.

Results

A high classification accuracy of 99.99% is achieved for both validation techniques. This is one of the first few studies to have employed deep learning algorithm coupled with ECG signals for the detection of HPT. Our results imply that the developed tool is useful in a hospital setting as an automated diagnostic tool, enabling the effortless detection of HPT using ECG signals.

中文翻译：

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使用卷积神经网络的高血压自动诊断工具

背景

当动脉内的血压（BP）升高时，就会发生高血压（HPT），从而导致心脏在更高的后负荷下更加努力地向心脏其他部位输送含氧血液。

目的

由于血压的波动，24小时动态血压监测已成为诊断HPT的有用工具，但因其不便而受到限制。因此，在这项研究中，使用了一种使用心电图（ECG）信号的自动诊断工具来自动检测HPT。

方法

预处理后的信号被馈送到卷积神经网络模型。该模型学习并识别独特的ECG签名，以对正常和高血压ECG信号进行分类。所提出的模型通过10倍评估，并保留了一项基于患者的验证技术。

结果

两种验证技术均达到99.99％的高分类精度。这是将深度学习算法与ECG信号结合使用以检测HPT的第一批研究之一。我们的结果表明，该开发工具可在医院环境中用作自动诊断工具，从而能够使用ECG信号轻松检测HPT。