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

Abstract

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.

Discrimination of Severity of Alzheimer’s Disease with Multiscale Entropy Analysis of EEG Dynamics

“…Similarly, Figure 4 illustrates that the performances in differentiating the HC, AD1, and AD2 groups do not improved from three to five scales. Indeed, the differentiating performances are high enough using 2 to 3 MSE scales and may not be positively correlated with the number of scales selected [18]. This implies that a simple model is sufficient to reflect the AD severity.…”

Alterations of Cardiovascular Complexity during Acute Exposure to High Altitude: A Multiscale Entropy Approach

“…This suggests a loss of cardiovascular complexity that mainly affects the faster components, probably associated with ventilation, while the cardiac complexity at longer scales is preserved. For some aspects, like the sympathetic and ventilatory responses to hypercapnia [ 32 ], the high-altitude condition may represent a model of heart failure and it is worth noting that heart failure patients, compared to healthy subjects, have lower MSE values over a broad range of scales that includes the HF band [ 33 ].…”

Special Issue on Signal Processing and Machine Learning for Biomedical Data

“…Chao et al [14] addressed discrimination between younger/older normal sinus rhythm (NSR) and congestive heart failure (CHF), by analyzing the electrocardiogram (ECG) signal. Using the multiscale entropy (MSE) algorithm, they extracted 20 features and conducted a feature selection.…”