Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure Article Swipe
Hsuan-Hao Chao
,
Chih-Wei Yeh
,
Chang Francis Hsu
,
Long Hsu
,
Sien Chi
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/app9173496
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/app9173496
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app9173496
- https://www.mdpi.com/2076-3417/9/17/3496/pdf?version=1566628268
- OA Status
- gold
- Cited By
- 7
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2970639953
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2970639953Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app9173496Digital Object Identifier
- Title
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Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart FailureWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-08-24Full publication date if available
- Authors
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Hsuan-Hao Chao, Chih-Wei Yeh, Chang Francis Hsu, Long Hsu, Sien ChiList of authors in order
- Landing page
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https://doi.org/10.3390/app9173496Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/9/17/3496/pdf?version=1566628268Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/9/17/3496/pdf?version=1566628268Direct OA link when available
- Concepts
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Support vector machine, Linear discriminant analysis, Artificial intelligence, Pattern recognition (psychology), Mean squared error, Heartbeat, Entropy (arrow of time), Mathematics, Statistics, Computer science, Machine learning, Computer security, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2023: 1, 2021: 1, 2020: 3, 2019: 2Per-year citation counts (last 5 years)
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58Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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