Affordable Access

deepdyve-link
Publisher Website

An Electrocardiographic System With Anthropometrics via Machine Learning to Screen Left Ventricular Hypertrophy among Young Adults

Authors
  • Lin, Gen-Min1, 2, 3
  • Liu, Kiang1
  • 1 Northwestern University Feinberg School of Medicine, USA
  • 2 Hualien Armed Forces General Hospital, Taiwan
  • 3 National Defense Medical Center, Taiwan
Type
Published Article
Journal
IEEE Journal of Translational Engineering in Health and Medicine
Publisher
IEEE
Publication Date
Apr 24, 2020
Volume
8
Identifiers
DOI: 10.1109/JTEHM.2020.2990073
PMID: 32419990
PMCID: PMC7224269
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

The aim of this study was to develop an ECG system with anthropometric data using machine learning to increase the accuracy and sensitivity for a screen for physiological and pathological left ventricular hypertrophy (LVH) among young adults. In a large sample of 2,196 males aged 17-45 years, the support vector machine (SVM) classifier was used as the machine learning method for 31 characteristics, including age, body height and body weight, in addition to 28 ECG parameters such as axes, intervals and voltages to link the output of LVH. Our system provides a novel screening tool using age, body height, body weight and ECG data to identify most of the LVH among young adults. It provides a fast, accurate and practical diagnosis tool to identify LVH.

Report this publication

Statistics

Seen <100 times