Cover Image

Development of a syncope classification algorithm from physiological signals acquired in tilt-table test

Syncope also known as transient loss of consciousness which caused problem to human daily life. Since machine learning is much more advanced, classification of syncope can be done with machine learning. Head-up tilt table test (HUTT) having a lengthy procedure and might causing patient to feel disco...

Full description

Saved in:
Bibliographic Details
Main Author: Gan, Ming Hong
Format: Final Year Project / Dissertation / Thesis
Published: 2023
Subjects:
R Medicine (General)
Online Access:http://eprints.utar.edu.my/5809/1/BI_1801320_Final_GAN_MING_HONG.pdf
http://eprints.utar.edu.my/5809/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-utar-eprints.5809
record_format eprints
spelling my-utar-eprints.58092023-08-08T12:16:30Z Development of a syncope classification algorithm from physiological signals acquired in tilt-table test Gan, Ming Hong R Medicine (General) Syncope also known as transient loss of consciousness which caused problem to human daily life. Since machine learning is much more advanced, classification of syncope can be done with machine learning. Head-up tilt table test (HUTT) having a lengthy procedure and might causing patient to feel discomfort during the test. Aim of this study is to design an algorithm which able to classify syncope patient based on their physiological signal. In this study, electrocardiogram (ECG) and blood pressure (BP) signal has been collected from 144 subjects with head-up tilt table test (HUTT) by clinicians. Several features have been extracted from heart rate, systolic and diastolic blood pressure. There are 8 set of feature selection model has built and a total of 24 set of classifiers with 3 different type of classification techniques were developed. Additionally, stratified 5-fold cross-validation was performed to evaluate the performance of proposed model. Features that selected for the classification is mean of systolic and diastolic blood pressure, standard deviation of real variability of diastolic blood pressure, and the mean of systolic blood pressure in low and high frequency ratio. The proposed model yielded the following result: 85.71% sensitivity, 91.43% specificity, 88.18% F1-score and 88.57% accuracy. Future work can be focus on utilise more different type of classifier and carry out external cross validation for achieving a better classification model. 2023 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5809/1/BI_1801320_Final_GAN_MING_HONG.pdf Gan, Ming Hong (2023) Development of a syncope classification algorithm from physiological signals acquired in tilt-table test. Final Year Project, UTAR. http://eprints.utar.edu.my/5809/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic R Medicine (General)
spellingShingle R Medicine (General)
Gan, Ming Hong
Development of a syncope classification algorithm from physiological signals acquired in tilt-table test
description Syncope also known as transient loss of consciousness which caused problem to human daily life. Since machine learning is much more advanced, classification of syncope can be done with machine learning. Head-up tilt table test (HUTT) having a lengthy procedure and might causing patient to feel discomfort during the test. Aim of this study is to design an algorithm which able to classify syncope patient based on their physiological signal. In this study, electrocardiogram (ECG) and blood pressure (BP) signal has been collected from 144 subjects with head-up tilt table test (HUTT) by clinicians. Several features have been extracted from heart rate, systolic and diastolic blood pressure. There are 8 set of feature selection model has built and a total of 24 set of classifiers with 3 different type of classification techniques were developed. Additionally, stratified 5-fold cross-validation was performed to evaluate the performance of proposed model. Features that selected for the classification is mean of systolic and diastolic blood pressure, standard deviation of real variability of diastolic blood pressure, and the mean of systolic blood pressure in low and high frequency ratio. The proposed model yielded the following result: 85.71% sensitivity, 91.43% specificity, 88.18% F1-score and 88.57% accuracy. Future work can be focus on utilise more different type of classifier and carry out external cross validation for achieving a better classification model.
format Final Year Project / Dissertation / Thesis
author Gan, Ming Hong
author_facet Gan, Ming Hong
author_sort Gan, Ming Hong
title Development of a syncope classification algorithm from physiological signals acquired in tilt-table test
title_short Development of a syncope classification algorithm from physiological signals acquired in tilt-table test
title_full Development of a syncope classification algorithm from physiological signals acquired in tilt-table test
title_fullStr Development of a syncope classification algorithm from physiological signals acquired in tilt-table test
title_full_unstemmed Development of a syncope classification algorithm from physiological signals acquired in tilt-table test
title_sort development of a syncope classification algorithm from physiological signals acquired in tilt-table test
publishDate 2023
url http://eprints.utar.edu.my/5809/1/BI_1801320_Final_GAN_MING_HONG.pdf
http://eprints.utar.edu.my/5809/
_version_ 1775627986411716608
score 13.211869

Similar Items