Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults
Mitral valve prolapse (MVP), known as balloon mitral valve, accounts for 2-4% of cases in the general population and is associated with several cardiac sequelae. A few studies have shown suboptimal results using electrocardiographic (ECG) machine learning to identify MVP in middle- or old...
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doaj-70c06c9885ca46a790fcf894a7ea6b3e2021-07-27T23:00:52ZengIEEEIEEE Access2169-35362021-01-01910313210314010.1109/ACCESS.2021.30980399490238Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young AdultsGen-Min Lin0https://orcid.org/0000-0002-5509-1056Huan-Chang Zeng1Department of Medicine, Hualien Armed Forces General Hospital, Hualien, TaiwanGENEus Medical Technology Company Ltd, Taipei, TaiwanMitral valve prolapse (MVP), known as balloon mitral valve, accounts for 2-4% of cases in the general population and is associated with several cardiac sequelae. A few studies have shown suboptimal results using electrocardiographic (ECG) machine learning to identify MVP in middle- or old-aged individuals; however, no studies have focused on young adults. The aim of this study is to develop an ECG-based system through machine learning to predict MVP in young adults. In a large military population of 2,206 males, aged 17–43 years, support vector machine (SVM), logistic regression (LR) and multilayer perceptron (MLP) classifiers are used as machine learning techniques for 26 ECG features and additional 6 simple biological parameters to link the output of MVP compared with a traditional ECG criterion of a negative T-axis in inferior limb leads. In the parasternal long-axis view of echocardiography, MVP is defined as a displacement of the anterior or posterior leaflet of the mitral valve to the mid portions of the annular hinge point >2 mm. The values of the area under the receiver operating characteristic curve are 74.59%, 74.16% and 73.02% in the proposed SVM, LR and MLP classifiers, respectively, which are better than 38.13% in the traditional ECG criterion for MVP. Our machine learning system provides a novel tool for screening MVP among young male adults. The proposed method can be an adjuvant to the physical findings for early detection of MVP prior to a confirmation by echocardiography for young male adults.https://ieeexplore.ieee.org/document/9490238/Echocardiographyelectrocardiographymachine learningmitral valve prolapseyoung adults |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gen-Min Lin Huan-Chang Zeng |
spellingShingle |
Gen-Min Lin Huan-Chang Zeng Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults IEEE Access Echocardiography electrocardiography machine learning mitral valve prolapse young adults |
author_facet |
Gen-Min Lin Huan-Chang Zeng |
author_sort |
Gen-Min Lin |
title |
Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults |
title_short |
Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults |
title_full |
Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults |
title_fullStr |
Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults |
title_full_unstemmed |
Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults |
title_sort |
electrocardiographic machine learning to predict mitral valve prolapse in young adults |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Mitral valve prolapse (MVP), known as balloon mitral valve, accounts for 2-4% of cases in the general population and is associated with several cardiac sequelae. A few studies have shown suboptimal results using electrocardiographic (ECG) machine learning to identify MVP in middle- or old-aged individuals; however, no studies have focused on young adults. The aim of this study is to develop an ECG-based system through machine learning to predict MVP in young adults. In a large military population of 2,206 males, aged 17–43 years, support vector machine (SVM), logistic regression (LR) and multilayer perceptron (MLP) classifiers are used as machine learning techniques for 26 ECG features and additional 6 simple biological parameters to link the output of MVP compared with a traditional ECG criterion of a negative T-axis in inferior limb leads. In the parasternal long-axis view of echocardiography, MVP is defined as a displacement of the anterior or posterior leaflet of the mitral valve to the mid portions of the annular hinge point >2 mm. The values of the area under the receiver operating characteristic curve are 74.59%, 74.16% and 73.02% in the proposed SVM, LR and MLP classifiers, respectively, which are better than 38.13% in the traditional ECG criterion for MVP. Our machine learning system provides a novel tool for screening MVP among young male adults. The proposed method can be an adjuvant to the physical findings for early detection of MVP prior to a confirmation by echocardiography for young male adults. |
topic |
Echocardiography electrocardiography machine learning mitral valve prolapse young adults |
url |
https://ieeexplore.ieee.org/document/9490238/ |
work_keys_str_mv |
AT genminlin electrocardiographicmachinelearningtopredictmitralvalveprolapseinyoungadults AT huanchangzeng electrocardiographicmachinelearningtopredictmitralvalveprolapseinyoungadults |
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