Automated diagnostic tool for hypertension using convolutional neural network

Copyright © 2020 Elsevier Ltd. All rights reserved..

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:126

Enthalten in:

Computers in biology and medicine - 126(2020) vom: 15. Nov., Seite 103999

Sprache:

Englisch

Beteiligte Personen:

Soh, Desmond Chuang Kiat [VerfasserIn]
Ng, E Y K [VerfasserIn]
Jahmunah, V [VerfasserIn]
Oh, Shu Lih [VerfasserIn]
Tan, Ru San [VerfasserIn]
Acharya, U Rajendra [VerfasserIn]

Links:

Volltext

Themen:

10-Fold validation
Automated diagnostic tool
Convolutional neural network
Hypertension
Journal Article
Leave one patient out validation
Masked hypertension

Anmerkungen:

Date Completed 21.06.2021

Date Revised 21.06.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1016/j.compbiomed.2020.103999

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM315627239