Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure

Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2019-09, Vol.9 (17), p.3496
Main Authors: Chao, Hsuan-Hao, Yeh, Chih-Wei, Hsu, Chang Francis, Hsu, Long, Chi, Sien
Format: Article
Language:English
Subjects:
Accuracy
Congestive heart failure
Dimensional analysis
Discriminant analysis
Entropy
Feature selection
Heart failure
Heart rate
heart rate variability
Learning algorithms
low-dimensional exhaustive search
Machine learning
Mortality
multiscale entropy
Older people
Searching
Sinuses
Support vector machines
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (
ISSN:2076-3417
2076-3417
DOI:10.3390/app9173496