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Open AccessArticle

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

1
Department of Electrophysics, National Chiao Tung University, Hsinchu 30010, Taiwan
2
Department of Photonics, National Chiao Tung University, Hsinchu 30010, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(17), 3496; https://doi.org/10.3390/app9173496
Received: 3 July 2019 / Revised: 20 August 2019 / Accepted: 21 August 2019 / Published: 24 August 2019
(This article belongs to the Special Issue Signal Processing and Machine Learning for Biomedical Data)
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 (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (≥55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age. View Full-Text
Keywords: heart rate variability; multiscale entropy; heart failure; machine learning; low-dimensional exhaustive search; feature selection heart rate variability; multiscale entropy; heart failure; machine learning; low-dimensional exhaustive search; feature selection
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Chao, H.-H.; Yeh, C.-W.; Hsu, C.F.; Hsu, L.; Chi, S. Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure. Appl. Sci. 2019, 9, 3496.

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